masked-autoencoder
2 free lessons tagged masked-autoencoder across AI. Each one is a short sequence of focused steps with narration and a five-question quiz at the end — take them in any order, no signup required.
Beyond Negatives: Non-Contrastive and Masked Methods
How to prevent collapse without negative samples. This lesson covers distillation methods (BYOL, SimSiam) that rely on stop-gradients and asymmetry, regularization methods (Barlow Twins, VICReg) that constrain the embedding's statistics, and the masked-modeling family (MAE, BERT), then ties them to JEPA and the energy-based view.
Why Predict Representations, Not Pixels
Self-supervised learning has three families: reconstruct the input, contrast augmented views, or predict in a learned embedding space. This lesson explains why the third, the joint-embedding predictive approach behind JEPA, sidesteps both wasted capacity on unpredictable pixels and hand-crafted augmentation biases.