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Sure! Here's an overview of Self-Supervised Learning (SSL) and Representation Learning, tailored for an educational or reference purpose. Let me know if you want it as a presentation, blog post, or formal report!
📘 Self-Supervised Learning (SSL)
🔍 What is Self-Supervised Learning?
Self-Supervised Learning is a type of machine learning where the system learns to understand data without needing manually labeled examples. It creates its own labels from the input data itself using pretext tasks.
💡 Key Idea
Train a model using pseudo-labels generated from the raw data. Once the model learns from this, the representations it forms can be transferred to downstream tasks like classification, detection, or recommendation.
🧠 Common Pretext Tasks
- Contrastive Learning: Pull together representations of similar inputs and push apart different ones (e.g., SimCLR, MoCo).
- Predictive Tasks: Predict missing parts of the data (e.g., predicting a masked word in BERT or image patches in MAE).
- Transformation Prediction: Predict which transformation (rotation, color jitter, cropping) has been applied.
🎯 Representation Learning
🔍 What is Representation Learning?
Representation Learning is about transforming raw input data (like images, text, or audio) into useful feature vectors or embeddings that capture meaningful information.
🤝 Relationship to SSL
Self-supervised learning is one way to do representation learning — by creating tasks that help models learn rich, generalized features without labeled data.
🎯 Goals
- Learn low-dimensional, information-rich representations
- Enable models to generalize well on unseen tasks
- Reduce need for large labeled datasets
🔧 Techniques in Representation Learning
1. Autoencoders
- Encoder compresses data, decoder reconstructs it.
- Latent vector in between is the learned representation.
2. Contrastive Methods
- Examples: SimCLR, MoCo, BYOL
- Use pairs of augmented views of the same data point
- Objective: Make the embeddings of similar views close together.
3. Masked Modeling
- Examples: BERT (text), MAE (images)
- Mask part of input and train model to reconstruct it.
- Forces model to understand global context.
4. Clustering-Based Methods
- Example: DeepCluster, SwAV
- Group similar representations and refine those groups during training.
🧪 Applications
- Computer Vision: Image classification, object detection, segmentation
- Natural Language Processing: Text classification, question answering (e.g., BERT, GPT pretraining)
- Speech & Audio: Speaker recognition, speech-to-text
- Recommender Systems: Learning user/item embeddings
🔮 Future Trends
- Multimodal SSL: Learning from images + text (e.g., CLIP, DALL·E)
- Scalable & Generalist Models: Training foundational models on massive datasets (e.g., GPT, SimCLR, MAE)
- Low-resource settings: Use SSL to reduce dependence on labeled data
Would you like a visual summary (slides, infographic), deeper dive into specific models (like SimCLR, BERT, etc.), or code examples in PyTorch or TensorFlow?