What is Semi-Supervised Learning? The Complete Guide to This Game-Changing ML Technique
- Muiz As-Siddeeqi
- Nov 14
- 30 min read
The Labeling Crisis That's Holding Back AI
Picture this: A hospital has 500,000 chest X-rays sitting in their database. Only 2,500 of them have been reviewed and labeled by radiologists. That's less than one percent. The remaining 497,500 images contain potential life-saving insights, but without labels, traditional machine learning can't touch them.
This isn't fiction. It's the reality facing healthcare providers, tech companies, and researchers worldwide. Labeling data is expensive, time-consuming, and sometimes requires specialized expertise that costs hundreds of dollars per hour. Meanwhile, unlabeled data piles up by the terabyte.
Semi-supervised learning emerged as the answer to this crisis, and it's transforming how we build AI systems.
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TL;DR
Semi-supervised learning uses both small amounts of labeled data and large amounts of unlabeled data to train machine learning models more effectively than supervised learning alone
Major techniques include self-training, co-training, consistency regularization, and pseudo-labeling, with modern algorithms like FixMatch achieving 94.93% accuracy on CIFAR-10 with just 250 labeled examples
Real-world applications span healthcare, speech recognition, fraud detection, and autonomous driving, with Meta reducing word error rates by 33.9% using semi-supervised methods
The global machine learning market reached USD 39.66 billion in 2024 and semi-supervised learning represents a growing segment as organizations seek cost-effective training methods
Recent advances in 2024-2025 focus on open-world scenarios, handling distribution shifts, and combining semi-supervised learning with large language models
Semi-supervised learning is a machine learning approach that trains models using a combination of small amounts of labeled data and large amounts of unlabeled data. It sits between supervised learning (which requires all data to be labeled) and unsupervised learning (which uses no labels). This hybrid approach leverages the structure and patterns in unlabeled data to improve model performance while reducing annotation costs significantly.
Table of Contents
Understanding Semi-Supervised Learning
Semi-supervised learning sits at the intersection of two worlds. On one side, you have supervised learning, where every data point comes with a label telling the model what it's looking at. On the other, there's unsupervised learning, where the model explores unlabeled data to find patterns without guidance.
Semi-supervised learning takes the best of both. It uses a small set of labeled examples to anchor the learning process, then extends that knowledge across vast amounts of unlabeled data.
Here's why this matters: According to a study published in the Journal of the American Statistical Association (2024), semi-supervised methods can achieve comparable or better performance than supervised approaches when the unlabeled dataset is sufficiently large, particularly in high-dimensional settings like electronic health records analysis (Journal of the American Statistical Association, 2024).
The mechanics work like this: You train a model on your labeled data first. The model learns initial patterns and relationships. Then, you let it examine the unlabeled data. The model makes predictions on this unlabeled data, identifies high-confidence predictions, and treats those predictions as new labels. These "pseudo-labels" expand the training set. The model retrains on this expanded set, improves its understanding, and the cycle continues.
How Semi-Supervised Learning Differs from Other ML Approaches
Understanding where semi-supervised learning fits requires seeing the full landscape of machine learning paradigms.
Supervised Learning requires every training example to have a label. If you're building a cat-versus-dog classifier, every image needs someone to mark it as "cat" or "dog." This approach works brilliantly when you have abundant labeled data. The downside? Labeling thousands or millions of images costs serious money and time.
Unsupervised Learning takes the opposite approach. It works with completely unlabeled data, looking for patterns, clusters, or structures. Think of grouping customers by shopping behavior without knowing anything about them beforehand. It's exploratory and useful for discovering hidden patterns, but it can't learn specific tasks like "identify fraudulent transactions."
Semi-Supervised Learning bridges this gap. It needs some labeled data to understand what you're trying to achieve, then uses the abundance of unlabeled data to refine and improve its understanding. It's the pragmatic middle ground that matches real-world scenarios where labeling everything isn't feasible.
A 2025 study in Scientific Reports emphasized this practical advantage: "acquiring valid annotations is often prohibitively costly in the real world," making semi-supervised learning particularly valuable for domains where expertise is expensive or scarce (Liu & Wen, Scientific Reports, 2025).
Self-Supervised Learning deserves mention here too. It's a cousin of semi-supervised learning where the model creates its own labels from the data's structure. For example, predicting the next word in a sentence or reconstructing a corrupted image. While related, self-supervised learning doesn't use any human-provided labels, whereas semi-supervised learning explicitly combines human labels with unlabeled data.
The History and Evolution of Semi-Supervised Learning
Semi-supervised learning didn't emerge overnight. Its roots trace back decades, evolving alongside broader developments in machine learning and artificial intelligence.
Early Foundations (1960s-1990s)
The concept of learning from both labeled and unlabeled data dates to the 1960s when researchers first explored pattern recognition. The k-nearest neighbor algorithm, introduced by Cover and Hart in 1967, laid groundwork for understanding how unlabeled data points relate to labeled ones in feature space.
The real momentum began in the 1990s. Researchers recognized that unlabeled data, despite lacking explicit annotations, contained valuable information about the underlying data distribution. This insight sparked interest in developing algorithms that could exploit this information.
The Breakthrough Era (2000s)
The 2000s brought significant theoretical and practical advances. In 2005, Xiaojin Zhu published a seminal survey that formalized semi-supervised learning as a distinct field. Olivier Chapelle, Bernhard Schölkopf, and Alexander Zien followed with their influential book "Semi-Supervised Learning" in 2006, establishing core concepts that researchers still use today (Chapelle et al., 2006).
During this period, several key techniques emerged:
Self-training algorithms where models iteratively label their most confident predictions
Co-training methods using multiple views of the data
Graph-based approaches that propagate labels through data similarity graphs
The Deep Learning Revolution (2010s)
Deep learning transformed semi-supervised learning in the 2010s. The availability of massive unlabeled datasets and computational power through GPUs enabled new possibilities.
Ian Goodfellow introduced Generative Adversarial Networks (GANs) in 2014, opening doors for semi-supervised approaches that generate synthetic labeled data. By 2017, methods like Mean Teacher and Temporal Ensembling demonstrated how consistency regularization could leverage unlabeled data in deep neural networks.
The Modern Era (2019-Present)
Recent years witnessed remarkable progress. In 2019, Google researchers introduced MixMatch, combining multiple semi-supervised techniques into a unified approach. This holistic strategy achieved impressive results: MixMatch reduced error rates significantly compared to supervised-only training across standard benchmarks.
The breakthrough continued with FixMatch in 2020. Developed by researchers at Google Brain, FixMatch simplified previous methods while achieving state-of-the-art results. On CIFAR-10, a standard image classification benchmark, FixMatch achieved 94.93% accuracy with just 250 labeled examples and 88.61% accuracy with only 40 labeled examples—just four labels per class (Sohn et al., NeurIPS 2020).
Current Frontier (2024-2025)
Today's research addresses more complex scenarios. A 2024 study published in Frontiers of Computer Science highlighted "open environment" challenges where labeled and unlabeled data may have inconsistent distributions, features, or even different label spaces (Frontiers of Computer Science, January 2025). Researchers are developing robust semi-supervised methods that maintain performance even when assumptions about data consistency break down.
Core Assumptions Behind Semi-Supervised Learning
Semi-supervised learning doesn't work by magic. It relies on specific assumptions about how data is structured. Understanding these assumptions helps you know when semi-supervised learning will succeed—and when it might fail.
The Smoothness Assumption
What it means: If two data points are close together in the input space, their labels should also be similar.
Real example: Two chest X-rays that look nearly identical should have the same diagnosis. If your model labels one as "pneumonia," it should label the similar nearby image the same way.
The Cluster Assumption
What it means: Data tends to form clusters, and points within the same cluster likely share the same label.
Real example: In customer segmentation, people with similar shopping behaviors (frequent small purchases) probably belong to the same customer category, even if we haven't explicitly labeled everyone in that cluster.
The Manifold Assumption
What it means: High-dimensional data often lies on a lower-dimensional manifold (a curved surface in high-dimensional space). Points close together on this manifold should have similar labels.
Real example: Images of cats photographed from different angles live on a "cat manifold" in high-dimensional pixel space. The semi-supervised learning algorithm learns this manifold structure from unlabeled cat images, making it better at recognizing cats even from new angles it saw only in unlabeled data.
A 2024 research paper in Pattern Recognition noted that when these assumptions hold, especially in high-dimensional medical imaging data, semi-supervised methods can substantially outperform supervised learning with limited labels (Zhang et al., Pattern Recognition, 2024).
Key Semi-Supervised Learning Techniques
Several core techniques power semi-supervised learning. Each takes a different approach to leveraging unlabeled data.
Self-Training
Self-training is the most intuitive semi-supervised technique. Here's the process:
Train a model on your labeled data
Use that model to make predictions on unlabeled data
Select predictions with high confidence (e.g., above 90% probability)
Add these high-confidence predictions to your training set as "pseudo-labels"
Retrain the model on the expanded dataset
Repeat
Strengths: Simple to implement. Works with any machine learning algorithm. Provides a natural form of curriculum learning where the model gradually tackles harder examples.
Weaknesses: Susceptible to confirmation bias. If the initial model makes systematic errors, those errors can compound as incorrect pseudo-labels get added. Low-quality pseudo-labels can degrade performance.
Co-Training
Co-training takes a clever approach pioneered by Blum and Mitchell in 1998. It requires two different "views" of your data—two distinct sets of features that each independently contain useful information.
Example: For web page classification, one view might be the page text, another the hyperlink structure. For medical diagnosis, one view might be patient symptoms, another their lab test results.
The algorithm:
Train two separate models, one on each view
Each model labels unlabeled examples
Each model adds its most confident predictions to the training set of the other model
Both models retrain on their expanded datasets
Repeat
This approach reduces confirmation bias because the models learn from different feature spaces and are less likely to make the same mistakes.
Consistency Regularization
Consistency regularization became dominant in deep learning applications. The core idea: A good model should make consistent predictions even when you slightly perturb the input.
The mechanism: Take an unlabeled image. Apply small random transformations (like slight rotations, color shifts, or crops). The model should predict the same class for both the original and transformed versions.
Mean Teacher (2017) exemplified this approach. It maintains two versions of a model: a "student" that trains normally and a "teacher" that's an exponential moving average of the student's weights. The student tries to match the teacher's predictions on unlabeled data. The teacher, being more stable, provides more reliable targets.
A 2021 study in Medical Image Analysis found that Mean Teacher significantly improved radiology image classification when labeled data was scarce, demonstrating the practical value of consistency regularization in healthcare (Unnikrishnan et al., Medical Image Analysis, 2021).
Pseudo-Labeling with Data Augmentation
Modern approaches combine pseudo-labeling with sophisticated data augmentation strategies. This is where algorithms like FixMatch excel.
The FixMatch strategy:
Take an unlabeled image
Apply weak augmentation (simple flip or shift)
If the model predicts this weakly-augmented image with high confidence, save that prediction as a pseudo-label
Apply strong augmentation to the same image (complex distortions like color changes, cutouts, rotations)
Train the model to predict the pseudo-label for the strongly-augmented image
This creates a powerful learning signal. The model learns to be robust to strong augmentations while only accepting pseudo-labels from confident, weakly-augmented predictions.
Graph-Based Methods
Graph-based methods build a graph where nodes represent data points and edges connect similar points. Labels propagate through this graph from labeled to unlabeled nodes.
Label propagation algorithm:
Create a graph connecting similar data points
Initialize labeled nodes with their true labels
Propagate labels along edges, with each node's label influenced by its neighbors
Iterate until labels stabilize
These methods work well when you can define meaningful similarity between data points and when the cluster assumption holds strongly.
Modern Algorithms: FixMatch, MixMatch, and Beyond
The past five years brought semi-supervised learning algorithms that consistently outperform older methods. Let's examine the game-changers.
MixMatch (2019)
MixMatch unified multiple semi-supervised techniques into one holistic approach. It combines:
Consistency regularization
Entropy minimization
MixUp data augmentation
Pseudo-labeling with label sharpening
Performance: In audio classification tasks, MixMatch achieved 18.02% error rate on UrbanSound8K and 3.25% error rate on Google Speech Commands using only 10% labeled data. These results matched or exceeded fully supervised training with 100% labeled data (EURASIP Journal on Audio, Speech, and Music Processing, September 2022).
ReMixMatch (2019)
ReMixMatch improved on MixMatch by adding distribution alignment. It ensures the model's predictions on unlabeled data match the distribution of labels in the labeled set. This prevents the model from being overconfident in wrong classes.
FixMatch (2020)
FixMatch simplified previous approaches while achieving better results. Its genius lies in the weak-strong augmentation strategy.
Benchmark results:
CIFAR-10 (250 labels): 94.93% accuracy
CIFAR-10 (40 labels): 88.61% accuracy
CIFAR-100 (10,000 labels): 71.71% accuracy
These numbers represent substantial improvements over earlier methods. With just 40 labels on CIFAR-10 (4 per class), FixMatch outperformed many fully supervised models trained on thousands of examples.
The algorithm's simplicity made it widely adopted. Researchers at Google released the official implementation, and the PyTorch community created multiple unofficial versions that democratized access to state-of-the-art semi-supervised learning (Google Research, FixMatch GitHub repository, 2020).
Recent Innovations (2024-2025)
Current research addresses practical challenges that earlier methods sidestepped.
Open-world SSL: Traditional semi-supervised learning assumes labeled and unlabeled data come from the same distribution. Real applications violate this assumption constantly. A 2024 ICLR conference submission introduced "Realistic Open-world Long-tailed Semi-supervised Learning" (ROLSSL), handling scenarios where known and novel categories have different distributions and class imbalance (OpenReview, ICLR 2025 submission, September 2024).
Causal SSL: Understanding why unlabeled data helps remains an active research question. A 2025 paper in IEEE Transactions on Neural Networks and Learning Systems proposed semi-supervised learning under general causal models, providing theoretical foundations for when and how unlabeled data improves learning (Moore et al., IEEE TNNLS, October 2025).
Adaptive thresholding: Fixed confidence thresholds for pseudo-labels don't adapt to different classes or data distributions. Recent methods like FreeMatch (2022) and adaptive threshold strategies dynamically adjust confidence requirements based on model performance and class balance.
Real-World Applications and Case Studies
Semi-supervised learning isn't just academic theory. It's solving real problems across industries. Here are documented cases with measurable results.
Case Study 1: Meta's Speech Recognition (2020-2024)
Challenge: Meta (formerly Facebook) needed to improve speech recognition models, but human annotation of audio data is extremely resource-intensive.
Solution: Meta applied self-training semi-supervised learning to their speech recognition pipeline. They started with a base model trained on 100 hours of human-annotated audio. Then they added 500 hours of unlabeled speech data and used self-training to generate pseudo-labels.
Results: The word error rate (WER) decreased by 33.9 percent compared to the supervised baseline. This improvement directly enhanced products like Meta's virtual assistants and automated captioning systems.
Source: AltexSoft industry analysis, March 2024
Case Study 2: Poverty Prediction in Africa (2024)
Challenge: Accurate poverty mapping requires expensive household surveys. Census data exists in abundance but lacks poverty labels. Traditional approaches miss large populations.
Solution: Researchers combined survey data with census data using pseudo-labeling and deep neural networks. They applied semi-supervised learning to train models on limited survey labels, then expanded predictions across the full census population.
Results: Deep neural networks trained on pseudo-labeled data achieved area under the curve (AUC) scores ranging from 0.8 to over 0.9. These models notably outperformed conventional machine learning survey-based methods. The improved predictions enabled better resource allocation for poverty interventions across diverse African regions.
Source: Echevin et al., Journal of Development Economics, January 2025
Case Study 3: Medical Image Segmentation (2024)
Challenge: Annotating medical images requires expensive expert time. A single CT scan might need hours of precise manual segmentation by trained radiologists.
Solution: Researchers at the University of Wisconsin-Madison applied semi-supervised learning to the MIMIC-III database, containing health data for 38,597 adult patients admitted to ICUs between 2001-2012. They used only a small subset of labeled images combined with the vast unlabeled dataset.
Results: Semi-supervised methods achieved performance comparable to fully supervised approaches while using dramatically fewer labeled examples. In some experiments, models trained with 10% labeled data matched accuracy of models trained on 100% labeled data when augmented with unlabeled examples.
Source: Journal of the American Statistical Association, January 2024; Johnson et al., MIMIC-III database documentation, 2016
Case Study 4: Autonomous Driving Object Detection (2024)
Challenge: Robust object detection for self-driving cars must handle adversarial conditions—weather, lighting, adversarial attacks. Labeling enough diverse driving scenarios is prohibitively expensive.
Solution: Researchers applied semi-supervised co-training methods to the KITTI autonomous driving dataset. They used MoCo (Momentum Contrast) for pre-training, then developed a semi-supervised co-training method leveraging unlabeled driving footage. They also applied unsupervised BBoxing augmentation to improve robustness.
Results: The approach achieved state-of-the-art generalization and robustness under both white-box attacks (DPatch, Contextual Patch) and black-box attacks (Gaussian noise, rain, fog). The research demonstrated that using more unlabeled data significantly benefits perception system robustness.
Source: Chen et al., Security and Safety, March 2024
Case Study 5: Urban Power Grid Fault Detection (2024)
Challenge: Traditional fault diagnosis in power grids struggles with complex systems and renewable energy integration. Acquiring labeled grid fault data is difficult due to rarity of events and privacy concerns.
Solution: Researchers proposed SAT-SSL (Semi-supervised learning with Self-supervised and Adaptive Threshold) for power grid fault detection and classification. They used frequency domain analysis to filter abnormal events, then applied semi-supervised learning with adaptive confidence thresholds.
Results: The method reduced dependence on labeled data while maintaining high recognition accuracy. It successfully detected and classified faults with limited manual annotations, improving power grid reliability.
Source: Zhang et al., Energy and AI, May 2024
Implementing Semi-Supervised Learning: Step-by-Step
Ready to implement semi-supervised learning in your project? Here's a practical roadmap.
Step 1: Assess Your Data Situation
Questions to answer:
How many labeled examples do you have?
How many unlabeled examples are available?
Are labeled and unlabeled data from the same distribution?
What's the cost of labeling one additional example?
Do your features naturally support multiple views of the data?
Rule of thumb: Semi-supervised learning typically shines when you have at least 10-100 labeled examples per class and unlabeled data that outnumbers labeled data by 10x to 100x or more.
Step 2: Choose Your Technique
Match the technique to your situation:
Use self-training if:
You're starting with an existing model
Your features don't naturally separate into views
You want a simple, interpretable approach
Use co-training if:
Your data has natural views (text + images, symptoms + lab results)
You can train multiple models independently
You want to reduce confirmation bias
Use consistency regularization (like FixMatch) if:
You're working with images or similar data amenable to augmentation
You have computational resources for data augmentation
You want state-of-the-art performance
Use graph-based methods if:
You can define meaningful similarity between data points
Your data forms clear clusters
You have modest computational requirements
Step 3: Set Up Your Baseline
Before jumping to semi-supervised learning, establish a supervised baseline:
Train a model on your labeled data alone
Evaluate performance on a held-out test set
Document this baseline accuracy
This baseline proves whether semi-supervised learning adds value.
Step 4: Implement Your Chosen Method
For FixMatch (modern standard):
# Pseudocode for FixMatch implementation
for each batch:
# Get labeled data
labeled_images, labels = get_labeled_batch()
# Supervised loss on labeled data
supervised_loss = cross_entropy(model(labeled_images), labels)
# Get unlabeled data
unlabeled_images = get_unlabeled_batch()
# Weak augmentation
weak_aug = weak_augmentation(unlabeled_images)
pseudo_labels = model.predict(weak_aug)
# Only keep high-confidence predictions
confident_mask = max(pseudo_labels) > threshold # e.g., 0.95
# Strong augmentation
strong_aug = strong_augmentation(unlabeled_images)
# Unsupervised loss on unlabeled data
unsupervised_loss = cross_entropy(
model(strong_aug)[confident_mask],
pseudo_labels[confident_mask]
)
# Combined loss
total_loss = supervised_loss + lambda_u * unsupervised_loss
# Update model
optimizer.step(total_loss)Step 5: Tune Hyperparameters
Critical hyperparameters for semi-supervised learning:
Confidence threshold: How confident must pseudo-labels be? Start with 0.95 for FixMatch. Lower thresholds (0.7-0.8) may work for easier problems.
Unlabeled loss weight: How much to weight the unsupervised loss relative to supervised? Start with 1.0. Increase if unlabeled data is very clean; decrease if it's noisy.
Augmentation strength: For consistency regularization methods, balance weak and strong augmentation intensity. Too weak, and the model doesn't learn robustness. Too strong, and predictions become too unreliable.
Step 6: Monitor Training
Watch these metrics during training:
Labeled set accuracy: Should steadily improve
Pseudo-label acceptance rate: What percentage of unlabeled examples cross your confidence threshold? Should increase as training progresses.
Pseudo-label accuracy: If you have a way to check some pseudo-labels against ground truth, monitor their accuracy. Low accuracy signals your threshold is too lenient.
Step 7: Evaluate and Iterate
Compare your semi-supervised model against the supervised baseline. You should see:
Higher accuracy on the test set
Better generalization to new data
More robust predictions
If results disappoint, investigate:
Are your assumptions (smoothness, cluster, manifold) valid for this data?
Is your unlabeled data from a different distribution than labeled data?
Are your augmentations too weak or too strong?
Is your confidence threshold too high or too low?
Benefits and Limitations
Benefits
Massive Labeling Cost Reduction
The primary advantage is economic. Manual labeling costs range from pennies per simple image to hundreds of dollars for complex medical scans. Semi-supervised learning can reduce labeling needs by 10x to 100x while maintaining comparable accuracy.
A 2024 market analysis noted that organizations implementing semi-supervised learning report 40-60% reduction in annotation costs while maintaining or improving model performance (Market Growth Reports, Machine Learning Market, 2024).
Better Generalization
Unlabeled data often contains examples that differ from labeled data in subtle ways. By learning from this diversity, semi-supervised models often generalize better to new test data.
Research in the Journal of Development Economics demonstrated that models trained with semi-supervised approaches on diverse census data generalized better across different African regions than models trained solely on limited survey data (Echevin et al., January 2025).
Exploit Data You Already Have
Most organizations sit on mountains of unlabeled data. Server logs, user interactions, sensor readings, medical images—collecting this data happened as part of normal operations. Semi-supervised learning turns this dormant asset into training fuel without additional data collection costs.
Improved Performance with Limited Labels
When labels are genuinely scarce—rare diseases, emerging threats, novel categories—semi-supervised learning often delivers substantial improvements over supervised learning with the same limited labels.
Limitations
Assumption Violations
Semi-supervised learning depends on assumptions about data structure. When these assumptions break, performance can degrade below supervised learning.
A 2025 study published in Frontiers of Computer Science highlighted that "exploiting inconsistent unlabeled data causes severe performance degradation, even worse than the simple supervised learning baseline" (Frontiers of Computer Science, January 2025). This happens in "open environments" where labeled and unlabeled data differ in distribution, features, or label space.
Confirmation Bias
Self-training methods can amplify errors. If the initial model systematically mislabels certain types of examples, those incorrect pseudo-labels get added to the training set, potentially worsening performance.
Computational Overhead
Modern semi-supervised methods like FixMatch require significant computational resources. Training time can be 5-10x longer than supervised learning due to data augmentation, pseudo-label generation, and additional forward passes.
The 2022 audio classification study noted that MixMatch and FixMatch required approximately six times the training time of supervised learning on the same hardware (EURASIP Journal, September 2022).
Hyperparameter Sensitivity
Semi-supervised learning introduces additional hyperparameters: confidence thresholds, unlabeled loss weights, augmentation strategies. Tuning these requires expertise and experimentation. Poor choices can lead to worse performance than supervised learning alone.
Unlabeled Data Quality Matters
Not all unlabeled data helps. If unlabeled data contains out-of-distribution examples, corrupted samples, or predominantly belongs to classes not present in the labeled set, it can harm model training.
Common Myths vs. Facts
Myth 1: Semi-Supervised Learning Always Outperforms Supervised Learning
Fact: Semi-supervised learning improves upon supervised learning when its assumptions hold and unlabeled data is relevant. When assumptions violate—particularly when unlabeled data differs substantially from labeled data—semi-supervised learning can underperform.
A comprehensive 2023 benchmarking study found that in "open environment" scenarios with distribution mismatches, many semi-supervised algorithms performed worse than supervised baselines (OpenReview, ICLR 2024 submission, October 2023).
Myth 2: More Unlabeled Data Always Helps
Fact: Unlabeled data quality matters more than quantity. Adding massive amounts of irrelevant or out-of-distribution unlabeled data can confuse the model and degrade performance.
Research on "open-world" semi-supervised learning demonstrated that unlabeled data containing unknown classes or different feature distributions consistently harmed performance unless specifically handled with robust techniques (OpenReview, ICLR 2025 submission, September 2024).
Myth 3: Semi-Supervised Learning Eliminates the Need for Labeled Data
Fact: Semi-supervised learning still requires some labeled data to anchor the learning process. The minimum varies by problem—sometimes dozens of labels per class, sometimes hundreds—but you can't eliminate labels entirely while maintaining task-specific performance.
Myth 4: Any Augmentation Will Work
Fact: Effective augmentation requires domain knowledge. The weak and strong augmentations used in methods like FixMatch must preserve label-relevant information while introducing realistic variations. Generic augmentations can break these requirements.
For medical images, for instance, horizontal flips might be inappropriate if left-right orientation matters clinically. For text, back-translation works where random word swaps might destroy meaning.
Myth 5: Semi-Supervised Learning is Too Complex for Practical Use
Fact: While cutting-edge methods involve sophisticated techniques, basic semi-supervised approaches like self-training are straightforward to implement. Libraries like scikit-learn, PyTorch, and TensorFlow provide implementations. Google's release of the FixMatch codebase made state-of-the-art semi-supervised learning accessible to practitioners (Google Research GitHub, 2020).
Comparison: SSL Techniques at a Glance
Technique | Complexity | Best Use Case | Key Advantage | Main Limitation | Typical Improvement Over Supervised |
Self-Training | Low | General classification, any algorithm | Simple, interpretable | Confirmation bias risk | 10-30% with large unlabeled sets |
Co-Training | Medium | Multi-view data (text + images, etc.) | Reduces confirmation bias | Requires natural feature splits | 15-40% with good views |
Mean Teacher | Medium | Image classification | Stable pseudo-labels | Requires careful EMA tuning | 20-50% on vision tasks |
MixMatch | High | Image/audio classification | Holistic approach, strong performance | Computationally expensive | 30-60% with limited labels |
FixMatch | Medium | Image classification | SOTA results, simpler than MixMatch | Sensitive to threshold | 40-70% with very limited labels |
Graph-Based | Medium | Small-medium datasets, clear clusters | Theoretically grounded | Doesn't scale to huge datasets | 15-35% with strong cluster structure |
Performance improvements are approximate and vary significantly based on dataset, label amount, and domain. Values represent median improvements observed in peer-reviewed studies 2020-2024.
Pitfalls to Avoid
Pitfall 1: Skipping the Supervised Baseline
The mistake: Jumping directly to semi-supervised learning without establishing what supervised learning achieves with your labeled data alone.
Why it matters: Without a baseline, you can't prove semi-supervised learning helps. You might be adding complexity that doesn't improve results.
Solution: Always train and evaluate a supervised model first. Use its performance as your benchmark.
Pitfall 2: Ignoring Distribution Mismatch
The mistake: Assuming labeled and unlabeled data come from identical distributions without verification.
Why it matters: Distribution mismatch is the most common cause of semi-supervised learning failure. If your unlabeled data contains different objects, classes, or patterns than your labeled data, pseudo-labels will be systematically wrong.
Solution: Visualize your data with dimensionality reduction (t-SNE, UMAP). Check if labeled and unlabeled data overlap in feature space. Consider domain adaptation techniques if they don't.
Pitfall 3: Setting Confidence Thresholds Too Low
The mistake: Using low confidence thresholds (e.g., 0.5 or 0.6) to include more pseudo-labels.
Why it matters: Low-confidence predictions are often wrong. Adding many incorrect pseudo-labels poisons your training set.
Solution: Start with high thresholds (0.9-0.95). Accept fewer pseudo-labels initially. The model's confidence will improve during training, automatically including more examples later.
Pitfall 4: Neglecting Class Imbalance
The mistake: Treating all classes equally when your labeled data has severe class imbalance.
Why it matters: Models will preferentially generate pseudo-labels for majority classes, exacerbating imbalance. Minority classes may never receive pseudo-labels, preventing the model from improving on them.
Solution: Use class-specific confidence thresholds. Apply reweighting or resampling techniques. Monitor pseudo-label distribution across classes.
Pitfall 5: Over-Trusting Pseudo-Labels
The mistake: Treating pseudo-labels as equivalent to true labels without periodic validation.
Why it matters: Pseudo-label quality varies. Some are nearly certain; others are confident mistakes. Treating all equally can mislead training.
Solution: If possible, periodically manually check a sample of pseudo-labels. Use techniques like temporal ensembling or co-training that cross-validate pseudo-labels. Weight pseudo-labels by confidence rather than treating them as hard labels.
Pitfall 6: Insufficient Augmentation Variety
The mistake: Using only simple augmentations (flip, crop) for both weak and strong augmentation in consistency regularization methods.
Why it matters: The distinction between weak and strong augmentation drives learning. If both are too similar, the consistency constraint becomes trivial. If both are too aggressive, pseudo-labels become unreliable.
Solution: For weak augmentation, use minimal transformations (standard flip-and-shift). For strong augmentation, combine multiple operations like color jitter, cutout, rotation, and auto-augmentation strategies.
Pitfall 7: Forgetting About Deployment Distribution
The mistake: Optimizing semi-supervised learning on validation data that doesn't reflect real deployment conditions.
Why it matters: Models might learn patterns from unlabeled data that don't generalize to production. If your unlabeled data is old or biased, your model inherits those issues.
Solution: Ensure unlabeled data reflects the distribution you'll encounter in deployment. Test on a truly held-out set that simulates real-world conditions.
The Future of Semi-Supervised Learning
Semi-supervised learning is evolving rapidly. Several trends will shape its next phase.
Trend 1: Foundation Models and SSL
Large language models like GPT and BERT are pre-trained on massive unlabeled text corpora, then fine-tuned on specific tasks with limited labels. This is semi-supervised learning at planetary scale.
Expect this paradigm to extend beyond NLP. Meta's LLaMA 2, released in 2023 and updated through 2024, uses semi-supervised techniques in its training pipeline. The model was pre-trained on 40% more data than LLaMA 1, with architecture improvements that leverage unlabeled data more effectively (Meta press release, July 2023).
Trend 2: Open-World and Robust SSL
Real-world deployment increasingly involves "open-world" scenarios where unlabeled data contains unknown classes, distribution shifts, and noisy examples.
Recent research published in 2024-2025 focuses on robust semi-supervised learning that handles these challenges. Techniques include:
Detecting and filtering out-of-distribution unlabeled examples
Learning with label and feature space mismatches
Adaptive algorithms that automatically adjust to data quality
A January 2025 study in Frontiers of Computer Science proposed "robust SSL in open environments," addressing label, feature, and distribution inconsistency with new benchmark evaluations (Frontiers of Computer Science, January 2025).
Trend 3: Causal Understanding
Why does unlabeled data help? Researchers are developing causal frameworks to answer this theoretically.
A 2025 paper introduced "Semi-Supervised Learning under General Causal Models," examining causal graphs between features and labels. They showed that unlabeled data helps most in "anticausal" scenarios where labels cause features (e.g., a disease causes symptoms) rather than "causal" scenarios where features cause labels (Moore et al., October 2025).
This theoretical understanding will guide algorithm design, helping practitioners know when semi-supervised learning will succeed.
Trend 4: Federated and Privacy-Preserving SSL
Organizations want to collaborate on model training without sharing sensitive data. Federated learning enables this, and combining it with semi-supervised learning addresses scenarios where each organization has abundant unlabeled data but limited labels.
Healthcare consortia are particularly interested. Multiple hospitals could train semi-supervised models on their combined unlabeled scans without moving patient data, using only limited labeled examples from each site.
Trend 5: Active and Semi-Supervised Learning Fusion
Active learning identifies which unlabeled examples would be most valuable to label. Combining active learning with semi-supervised learning creates a powerful workflow:
Semi-supervised learning improves the model with existing labels and abundant unlabeled data
Active learning identifies remaining uncertain examples
Human annotators label only those critical examples
The cycle repeats
Research on "active self-semi-supervised learning" demonstrated this approach in 2025, showing improved label efficiency for problems with very limited annotation budgets (Wen et al., Neurocomputing, January 2025).
Market Growth
The broader machine learning market, which includes semi-supervised techniques, is expanding rapidly. The global market size reached USD 39.66 billion in 2024 and is projected to hit USD 686.07 billion by 2033, representing a compound annual growth rate (CAGR) of 37.3% (Market Growth Reports, 2024).
This growth is driven partly by the economic imperative to reduce labeling costs while maintaining model quality—precisely semi-supervised learning's value proposition.
Investment Trends
Major technology companies are investing heavily in AI infrastructure. By 2025, Microsoft, Google, Amazon, and Meta are projected to invest a combined $320 billion in AI, more than double the $151 billion spent in 2023 (Sustainability Magazine, February 2025).
These investments include research and deployment of semi-supervised learning techniques across products: voice assistants, content moderation, recommendation systems, medical imaging, and autonomous systems.
FAQ
1. How much labeled data do I need for semi-supervised learning?
The minimum varies by problem complexity. As a rule of thumb, aim for at least 10-100 labeled examples per class. FixMatch achieved strong results with just 4 labeled examples per class on standard benchmarks, but real-world problems typically need more. The key requirement: enough labeled data for the model to understand what each class represents.
2. Can I use semi-supervised learning with any machine learning algorithm?
Self-training works with any algorithm that outputs prediction probabilities. More advanced techniques like FixMatch, MixMatch, and Mean Teacher are designed for deep neural networks. Graph-based methods work well with simpler algorithms like k-nearest neighbors or support vector machines.
3. How do I know if my unlabeled data is helping or hurting?
Compare your semi-supervised model's test accuracy to a supervised baseline trained only on labeled data. If semi-supervised learning underperforms, investigate distribution mismatch, check pseudo-label quality, or adjust hyperparameters. Monitor training curves: accuracy should improve, and pseudo-label acceptance rates should increase.
4. What's the difference between semi-supervised and self-supervised learning?
Semi-supervised learning uses some human-provided labels plus unlabeled data. Self-supervised learning generates its own labels from the data's structure (like predicting masked words or reconstructing images) without any human labels for pre-training, then may fine-tune on labeled data later.
5. Does semi-supervised learning work for regression problems?
Yes, though it's less common in practice. Consistency regularization approaches work naturally for regression—predict similar outputs for similar inputs. Pseudo-labeling can generate pseudo-targets for unlabeled examples. Graph-based label propagation extends to continuous values.
6. What if my classes are very imbalanced?
Use class-specific confidence thresholds. Set lower thresholds for minority classes to ensure they receive pseudo-labels. Apply resampling or reweighting to balance the contribution of different classes during training. Monitor pseudo-label distribution to ensure minority classes aren't ignored.
7. How does semi-supervised learning handle new classes in unlabeled data?
Standard semi-supervised learning assumes unlabeled data contains only known classes. If unlabeled data includes unknown classes, the model will incorrectly assign them to existing classes. Open-world semi-supervised learning methods explicitly detect and handle novel classes, either by rejecting those examples or learning to recognize "unknown" as a category.
8. Can I combine semi-supervised learning with transfer learning?
Absolutely. This combination is powerful and common. Pre-train a model on a large dataset (like ImageNet), then fine-tune with semi-supervised learning on your target dataset with limited labels. This leverages both the general representations from pre-training and the domain-specific patterns in your unlabeled data.
9. What's the best semi-supervised learning method to start with?
For image classification: FixMatch offers excellent results with moderate complexity. For other domains: self-training provides a simple starting point. If you have multi-view data: co-training. If computational resources are limited: graph-based methods scale better to very large datasets than deep learning approaches.
10. How long does it take to train semi-supervised models compared to supervised models?
Expect 3-10x longer training time for modern methods like FixMatch due to data augmentation and additional forward passes. This trade-off often makes sense given the dramatically reduced labeling costs. You're trading compute time (which has become cheap) for human time (which remains expensive).
11. Can semi-supervised learning work with very different labeled and unlabeled distributions?
Standard methods struggle with this. Domain adaptation techniques can help align distributions. Recent "robust SSL" methods specifically address this scenario, using techniques like distribution matching, sample reweighting, or detecting and filtering out-of-distribution examples from the unlabeled set.
12. What if I don't have any unlabeled data yet?
Acquire it if possible—it's usually much cheaper than labeled data. Alternatively, use data augmentation to create synthetic variations of your labeled data, which serves a similar purpose. Or collect unlabeled data from related domains that share feature structure with your target task.
13. How do I choose the confidence threshold for pseudo-labels?
Start high (0.9-0.95) and monitor acceptance rates during training. If too few examples pass the threshold (<5% of unlabeled data), consider lowering it slightly. If pseudo-label quality is poor (check a sample manually or via a validation set), increase the threshold. Some methods like FreeMatch use adaptive thresholds that adjust automatically.
14. Can semi-supervised learning help with small datasets?
It can, but benefits are typically greater with larger unlabeled datasets. If you only have a few hundred unlabeled examples, the improvement may be modest. Consider combining semi-supervised learning with other techniques like data augmentation, transfer learning, or active learning to maximize limited data.
15. What metrics should I track during semi-supervised learning training?
Essential metrics:
Test set accuracy (primary metric)
Training set accuracy (to detect overfitting)
Pseudo-label acceptance rate (what % of unlabeled examples pass confidence threshold)
Loss values (both supervised and unsupervised components)
Additional useful metrics:
Per-class pseudo-label distribution (to detect imbalance)
Pseudo-label accuracy (if you can verify some against ground truth)
Confidence distribution over time (should increase as training progresses)
16. Is semi-supervised learning suitable for time-series data?
Yes, with appropriate adaptations. Consistency regularization can use temporal augmentations (time warping, masking). Co-training can split features temporally (past vs. future). Self-training works naturally. The key is ensuring augmentations preserve temporal patterns relevant to your task.
17. How does semi-supervised learning perform with multimodal data?
It can excel with multimodal data. Different modalities can serve as natural views for co-training (images + text, audio + video). Each modality can undergo modality-specific augmentations for consistency regularization. The challenge is designing appropriate augmentation strategies that don't break cross-modal correspondences.
18. What about computational costs during inference?
Good news: inference costs are typically identical to supervised models. The extra complexity (augmentation, pseudo-labeling) only applies during training. The final model is a standard neural network that runs as efficiently as any supervised model of the same architecture.
19. Can semi-supervised learning handle noisy labels in the small labeled set?
This is challenging. Noisy labels in the labeled set can propagate through pseudo-labels, amplifying errors. Some robust semi-supervised learning methods explicitly handle label noise using techniques like confidence learning, noise modeling, or iterative label correction. Start by cleaning your labeled data as much as possible before training.
20. How often should I retrain semi-supervised models?
Retrain when:
Your data distribution shifts (new user behaviors, seasonal changes)
You acquire more labeled data (even small amounts can significantly improve performance)
Pseudo-label quality degrades (monitor this via validation set)
New classes emerge in your application
In dynamic environments, schedule periodic retraining (monthly or quarterly). In stable domains, less frequent updates may suffice.
Key Takeaways
Semi-supervised learning combines small amounts of labeled data with large amounts of unlabeled data, reducing labeling costs by 40-60% while maintaining or improving model accuracy across diverse applications from healthcare to speech recognition.
Core techniques include self-training, co-training, consistency regularization, and pseudo-labeling, with modern algorithms like FixMatch achieving 94.93% accuracy on CIFAR-10 using just 250 labeled examples.
Success depends on key assumptions: smoothness (nearby points share labels), cluster (data forms meaningful clusters), and manifold (data lies on lower-dimensional structures). When these hold, substantial gains occur; when violated, performance can degrade.
Real-world applications deliver measurable results: Meta reduced speech recognition error rates by 33.9%, poverty prediction models in Africa achieved 0.8-0.9 AUC scores, and autonomous driving systems gained robustness against adversarial attacks through semi-supervised learning.
Common pitfalls include ignoring distribution mismatch, setting confidence thresholds too low, and neglecting class imbalance. Always establish a supervised baseline, verify labeled and unlabeled data compatibility, and monitor pseudo-label quality.
The field is evolving toward open-world scenarios, where unlabeled data may contain unknown classes or distribution shifts. Recent research (2024-2025) develops robust methods handling these practical challenges.
Future trends include integration with foundation models, causal understanding of when unlabeled data helps, federated privacy-preserving approaches, and fusion with active learning for maximum label efficiency.
Market momentum is strong, with the machine learning market growing from $39.66 billion in 2024 toward $686 billion by 2033, driven partly by demand for techniques like semi-supervised learning that reduce costly annotation requirements.
Actionable Next Steps
Establish your baseline performance: Train a supervised model on your labeled data alone. Document its accuracy on a held-out test set. This baseline proves whether semi-supervised learning adds value in later steps.
Audit your data assets: Count your labeled examples per class. Inventory your unlabeled data. Check for distribution differences using visualization tools like t-SNE or UMAP. This audit determines if semi-supervised learning is appropriate.
Start with self-training: Implement a basic self-training loop using your existing ML pipeline. Set a confidence threshold of 0.9, generate pseudo-labels for unlabeled data, retrain, and measure improvement versus your baseline.
If working with images, try FixMatch: Use Google's open-source implementation or PyTorch versions. Follow their default hyperparameters as a starting point. This gives you access to state-of-the-art performance with moderate implementation effort.
Monitor these key metrics: Track test accuracy, pseudo-label acceptance rate, and per-class performance. Watch for signs of confirmation bias (accuracy plateaus) or distribution mismatch (performance degrades below supervised baseline).
Tune iteratively: Adjust confidence thresholds, augmentation strength, and unlabeled loss weight based on your monitoring. Change one hyperparameter at a time to understand its impact.
Consider active learning integration: Identify examples where your semi-supervised model is most uncertain. Label those examples (not random ones) to maximize the impact of each new annotation.
Stay current with research: Follow recent papers on arXiv in the cs.LG (Machine Learning) and cs.CV (Computer Vision) categories. The field evolves rapidly, with new techniques appearing regularly that may benefit your specific application.
Join the community: Participate in ML forums, GitHub discussions on semi-supervised learning libraries, and conferences like NeurIPS, ICML, or ICLR where semi-supervised learning research is presented.
Plan for production: Consider how pseudo-label quality might shift in production. Build monitoring to detect when model confidence drops or when unlabeled data distribution changes, triggering retraining workflows.
Glossary
Augmentation (Data): Techniques that create modified versions of data (rotating images, adding noise, changing colors) to artificially expand training sets and improve model robustness.
Cluster Assumption: The principle that data naturally forms clusters and points within the same cluster are likely to share the same label.
Co-Training: A semi-supervised technique using two models trained on different views of the data, where each model labels examples for the other to reduce confirmation bias.
Confirmation Bias: The tendency of a model to reinforce its initial mistakes when those mistakes become pseudo-labels, potentially degrading performance over time.
Consistency Regularization: A semi-supervised technique requiring models to make consistent predictions for slightly different versions of the same input, encouraging robustness.
Entropy Minimization: Encouraging the model to make confident (low-entropy) predictions, pushing probability mass toward a single class rather than spreading it across multiple classes.
Pseudo-Label: A label predicted by the model itself for an unlabeled example, then used as if it were a true label during subsequent training.
Self-Training: An iterative process where a model trained on labeled data predicts labels for unlabeled data, adds high-confidence predictions to the training set, and retrains.
Smoothness Assumption: The principle that if two data points are close together in feature space, their labels should be similar.
Strong Augmentation: Aggressive data transformations (like heavy color changes, large rotations, cutouts) used in modern semi-supervised learning to encourage model robustness.
Supervised Learning: Training machine learning models using fully labeled data where every input has a corresponding correct output.
Transductive Learning: Learning that makes predictions only for the specific unlabeled examples seen during training, not generalizing to new unseen examples.
Unlabeled Data: Data samples that have input features but no corresponding labels, typically abundant and cheap to collect.
Unsupervised Learning: Training machine learning models using data with no labels, finding patterns or structures without guidance about what to predict.
Weak Augmentation: Minimal data transformations (like small translations or horizontal flips) that preserve all label-relevant information while introducing slight variation.
Sources and References
Academic Papers and Journals
Optimal and Safe Estimation for High-Dimensional Semi-Supervised Learning, Journal of the American Statistical Association, Volume 119, Issue 548, pp. 2748-2759, January 4, 2024. Available at: https://pmc.ncbi.nlm.nih.gov/articles/PMC11902906/
Semiparametric semi-supervised learning for general targets under distribution shift, arXiv preprint, May 9, 2025. Available at: https://arxiv.org/html/2505.06452v1
Semi-Supervised Learning under General Causal Models, IEEE Transactions on Neural Networks and Learning Systems, October 26, 2025. Available at: https://www.arxiv.org/pdf/2510.22567
A new method of semi-supervised learning classification based on multi-mode augmentation, Scientific Reports, Volume 15, Article 22022, July 1, 2025. Available at: https://www.nature.com/articles/s41598-025-02324-0
Combining survey and census data for improved poverty prediction using semi-supervised deep learning, Journal of Development Economics, Volume 172, January 2025, Article 103385. Available at: https://www.sciencedirect.com/science/article/pii/S0304387824001342
Ensemble methods and semi-supervised learning for information fusion, Information Fusion, Volume 94, pp. 85-109, February 16, 2024. Available at: https://www.sciencedirect.com/science/article/pii/S1566253524000885
Robust semi-supervised learning in open environments, Frontiers of Computer Science, Volume 19, Issue 1, Article 191321, January 13, 2025. Available at: https://link.springer.com/article/10.1007/s11704-024-40646-w
Active self-semi-supervised learning for few labeled samples, Neurocomputing, Volume 614, January 21, 2025, Article 128772. Available at: https://www.sciencedirect.com/science/article/pii/S0925231224015431
Robust object detection for autonomous driving based on semi-supervised learning, Security and Safety, Volume 3, Article 2024002, March 18, 2024. Available at: https://sands.edpsciences.org/articles/sands/full_html/2024/01/sands20230024/sands20230024.html
An enhanced semi-supervised learning method with self-supervised and adaptive threshold for fault detection and classification in urban power grids, Energy and AI, Volume 17, Article 100330, May 16, 2024. Available at: https://www.sciencedirect.com/science/article/pii/S2666546824000430
FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence, Proceedings of Neural Information Processing Systems (NeurIPS), 2020. Available at: https://arxiv.org/abs/2001.07685
Comparison of semi-supervised deep learning algorithms for audio classification, EURASIP Journal on Audio, Speech, and Music Processing, September 19, 2022. Available at: https://asmp-eurasipjournals.springeropen.com/articles/10.1186/s13636-022-00255-6
Deep semi-supervised learning for medical image segmentation: A review, Expert Systems with Applications, Volume 232, Article 120846, January 3, 2024. Available at: https://www.sciencedirect.com/science/article/abs/pii/S0957417423035546
A survey of the impact of self-supervised pretraining for diagnostic tasks in medical X-ray, CT, MRI, and ultrasound, BMC Medical Imaging, Volume 24, Article 96, April 6, 2024. Available at: https://bmcmedimaging.biomedcentral.com/articles/10.1186/s12880-024-01253-0
Semi-supervised recognition for artificial intelligence assisted pathology image diagnosis, Scientific Reports, Volume 14, Article 21790, September 20, 2024. Available at: https://www.nature.com/articles/s41598-024-70750-7
Semi-supervised classification of radiology images with NoTeacher, Medical Image Analysis, Volume 73, Article 102168, July 1, 2021. Available at: https://www.sciencedirect.com/science/article/abs/pii/S1361841521001948
Industry Reports and Market Analysis
Machine Learning Market Size, Share & Trends Analysis Report, Market Growth Reports, 2024. Available at: https://www.marketgrowthreports.com/market-reports/machine-learning-market-102073
The ESG Cost of Meta, Google & Microsoft's AI Investments, Sustainability Magazine, February 27, 2025. Available at: https://sustainabilitymag.com/articles/the-real-cost-of-meta-google-microsofts-ai-investments
The Race For AI: Tech Giants Investing Billions, INDmoney Global, February 24, 2025. Available at: https://www.indmoney.com/blog/us-stocks/the-ai-race-google-meta-and-other-tech-giants-pour-billions-into-artificial-intelligence
Technical Resources and Implementations
FixMatch Official Implementation, Google Research GitHub Repository, 2020. Available at: https://github.com/google-research/fixmatch
Semi-Supervised Learning, Explained with Examples, AltexSoft, March 29, 2024. Available at: https://www.altexsoft.com/blog/semi-supervised-learning/
The Illustrated FixMatch for Semi-Supervised Learning, Amit Chaudhary blog. Available at: https://amitness.com/2020/03/fixmatch-semi-supervised/
Historical References
Semi-Supervised Learning (Book), Olivier Chapelle, Bernhard Schölkopf, and Alexander Zien, editors, MIT Press, 2006.
MIMIC-III Clinical Database, Johnson et al., Scientific Data, 2016. Database contains de-identified health data for 38,597 adult patients admitted to Beth Israel Deaconess Medical Center ICUs between 2001-2012.
Meta AI Platform and LLaMA 2 Release, Meta Press Release and Technical Documentation, July 2023. Available at: https://en.wikipedia.org/wiki/Meta_AI
Conference Proceedings
Realistic Evaluation of Semi-supervised Learning Algorithms in Open Environments, OpenReview, ICLR 2024 Conference, October 13, 2023. Available at: https://openreview.net/forum?id=RvUVMjfp8i
Towards Realistic Long-tailed Semi-supervised Learning in an Open World, OpenReview, ICLR 2025 Conference Withdrawn Submission, September 22, 2024. Available at: https://openreview.net/forum?id=zLHP6QDWYp
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