Advanced Training
Self-Supervised Learning in Deep Learning — Learning Without Labels
Self-supervised learning learns powerful representations from unlabeled data by solving pretext tasks. From contrastive learning to masked image modeling, SSL bridges the gap between supervised and unsupervised learning, enabling models to leverage massive unlabeled datasets.
- Key point 1 — Contrastive learning pulls positives together and pushes negatives apart
- Key point 2 — MAE masks 75% of patches and reconstructs — efficient and effective
- Key point 3 — Linear evaluation protocol standardizes comparison of learned representations
"The best representations come from the data itself."
Self-Supervised Learning
Self-supervised learning learns useful representations from unlabeled data by solving pretext tasks. It bridges the gap between supervised and unsupervised learning, enabling models to leverage massive unlabeled datasets.
The Self-Supervised Paradigm
Contrastive Learning
SimCLR
MoCo (Momentum Contrast)
Masked Image Modeling
BEiT (Bidirectional Encoder representation from Image Transformers)
Comparison of Methods
| Method | Type | Masking | Negatives | Batch Size | Performance |
|---|---|---|---|---|---|
| SimCLR | Contrastive | None | In-batch | 4096 | Good |
| MoCo v2 | Contrastive | None | Queue (65K) | 256 | Better |
| SwAV | Contrastive | None | Prototypes | 4096 | Better |
| MAE | Masked | 75% | None | 1024 | Excellent |
| BEiT | Masked | 40% | None | 1024 | Excellent |
| DINO | Self-distillation | None | Self | 1024 | Excellent |
PyTorch Implementation
Practice Exercises
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SimCLR ablation: Experiment with different augmentations. Which ones matter most?
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Linear evaluation: Train SimCLR on CIFAR-10, then freeze encoder and train linear classifier.
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MAE vs SimCLR: Compare representations using linear probing on CIFAR-10.
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Visualization: Plot attention maps from DINO. Does the model attend to objects?
Key Takeaways
What to Learn Next
-> DL Systems Design Master distributed training, monitoring, and production deployment of deep learning models.
-> Model Compression Make deep learning models fast and efficient for production deployment.
-> Neural Architecture Search Let AI design its own neural networks through automated search.
-> CNN Architecture Deep Dive Master convolutional layers, pooling, and modern CNN architectures.
-> Attention Mechanisms Discover how attention solves the information bottleneck in sequence models.
-> Vision Transformers Apply Transformer architecture to image recognition by treating patches as tokens.