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Semi-Supervised Learning

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Absolutely! Here's a complete and easy-to-understand breakdown of Semi-Supervised Learning (SSL)—perfect for notes, study, or presentations.

🧠 Semi-Supervised Learning (SSL)

💡 What is Semi-Supervised Learning?

Semi-Supervised Learning is a machine learning paradigm that uses a small amount of labeled data and a large amount of unlabeled data to train a model.

It’s a hybrid between supervised and unsupervised learning.

🤔 Why Use Semi-Supervised Learning?

  • Labeling data is expensive and time-consuming (especially in fields like medical imaging, speech recognition).
  • Unlabeled data is cheap and widely available.
  • SSL helps leverage the hidden structure in the data to improve learning.

🧩 SSL in Action

Imagine you have:

  • ✅ 500 labeled images of cats and dogs
  • ❓ 10,000 unlabeled images

SSL aims to use all 10,500 images to learn a better classifier than using just the 500 labeled ones.

⚙️ Common Approaches

1. Self-training

  • Train a model on labeled data
  • Use it to label the unlabeled data (pseudo-labels)
  • Retrain using both labeled and confidently pseudo-labeled data

2. Consistency Regularization

  • Model should produce similar outputs for perturbed versions of the same input
  • Example: Add noise to input → output shouldn’t change much

3. Pseudo-Labeling

  • Assign labels to unlabeled data using the model’s own high-confidence predictions
  • Combine with labeled data for further training

4. Graph-Based SSL

  • Represent data as a graph
  • Nodes: data points; Edges: similarity
  • Labels are propagated through the graph

5. Generative Models

  • Use models like VAEs or GANs to learn a representation of the data
  • Combine supervised and unsupervised objectives

🔥 Popular SSL Algorithms

Algorithm Description
FixMatch Combines pseudo-labeling with consistency
Mean Teacher Uses exponential moving average of model weights for targets
Virtual Adversarial Training (VAT) Adds adversarial noise to enforce output consistency
Label Propagation Classic graph-based approach
Semi-Supervised GANs GANs where discriminator also classifies

🧰 Real-World Applications

Domain Use Case
Computer Vision Image classification with limited labels
NLP Sentiment analysis, spam detection
Medical Imaging Diagnosing with few expert-labeled scans
Speech Recognition Training with limited transcribed data

📊 Pros & Cons

✅ Pros ❌ Cons
Less reliance on labeled data Can propagate incorrect labels
Improves generalization Sensitive to model confidence
Makes use of large unlabeled datasets Can be harder to evaluate

🧠 Intuition Behind SSL

  • Cluster Assumption: Points in the same cluster likely belong to the same class.
  • Manifold Assumption: Data lies on a lower-dimensional manifold, and labels change smoothly on this manifold.
  • Smoothness Assumption: Nearby points in input space should have the same label.

🧪 Simple SSL Workflow

  1. Start with labeled + unlabeled data
  2. Train initial model on labeled data
  3. Predict labels for unlabeled data (pseudo-labels)
  4. Combine confident pseudo-labeled data with original labeled data
  5. Retrain the model with the expanded dataset

🧰 Example: Pseudo-Labeling in PyTorch (Conceptual)

# Step 1: Train on labeled data
model.train()
model.fit(X_labeled, y_labeled)

# Step 2: Generate pseudo-labels
pseudo_labels = model.predict(X_unlabeled)
confidence = model.predict_proba(X_unlabeled).max(axis=1)

# Step 3: Filter confident predictions
confident_indices = confidence > 0.9
X_pseudo = X_unlabeled[confident_indices]
y_pseudo = pseudo_labels[confident_indices]

# Step 4: Combine and retrain
X_combined = np.concatenate([X_labeled, X_pseudo])
y_combined = np.concatenate([y_labeled, y_pseudo])

model.fit(X_combined, y_combined)

📚 Summary Table

Feature Semi-Supervised Learning
Labeled Data Small amount
Unlabeled Data Large amount
Goal Improve model with limited labels
Common Methods Self-training, consistency, GANs
Key Assumptions Smoothness, manifold, clustering

Let me know if you want:

  • Visuals to illustrate the SSL process
  • A code notebook for SSL experiments
  • A comparison to supervised and unsupervised learning
  • A cheat sheet or mini quiz for revision

Happy to help with whatever format you're using to learn or teach!