Self-Supervised Learning — Complete Guide

Self-supervised learning creates labels from the data itself, enabling training on massive unlabeled datasets.

Why Self-Supervised?

Labeled data: Expensive, scarce
Unlabeled data: Abundant, free

Self-supervised: Create pseudo-labels from data
├─ Masked token prediction (BERT, GPT)
├─ Contrastive learning (SimCLR, CLIP)
├─ Next token prediction (GPT)
└─ Image rotation prediction

Approaches

Contrastive Learning:
├─ Similar pairs → close in embedding space
├─ Dissimilar pairs → far apart
├─ SimCLR, MoCo, CLIP
└─ Works for images and text

Masked Modeling:
├─ Mask parts of input, predict them
├─ BERT: Mask tokens → predict
├─ MAE: Mask image patches → predict
└─ GPT: Predict next token

Pretext Tasks:
├─ Predict rotation
├─ Predict relative position
├─ Solve puzzles
└─ Fill in gaps

Key Takeaways

1. Self-supervised learning creates labels from data
2. Contrastive learning learns by comparing pairs
3. Masked modeling learns by predicting hidden parts
4. GPT and BERT use self-supervised pre-training
5. CLIP learns vision-language alignment
6. Self-supervised learning enables foundation models
7. Pre-training + fine-tuning is the dominant paradigm
8. Self-supervised learning reduces labeled data needs