MIT 6.7960 L11: Representation Learning (Reconstruction-Based) — Autoencoders, VQ, Self-Supervision
Representation learning compresses raw data into a 'useful' vector: autoencoders force a meaningful latent space via reconstruction, VQ discretizes it into a codebook, and self-supervision turns 'mask-and-reconstruct' into free supervision.