2021 was the year diffusion models surpassed GANs, self-supervised learning made theoretical breakthroughs, and reinforcement learning confronted weaknesses in its evaluation methodology. NeurIPS received a then-record 9,122 submissions, ICLR’s Score-Based Generative Modeling paper became a theoretical foundation for the diffusion ecosystem, and ICML delivered substantial work on optimization theory and the dynamics of self-supervised learning.
2021 was a dividing line for major AI conferences. Transformers spread from NLP throughout computer vision and time-series research, self-supervised learning became the most common cross-conference theme, and a diffusion model won an ICLR Outstanding Paper award before anyone realized it would displace GANs. Meanwhile, GNNs and federated learning reached historic peaks in paper volume before beginning to decline.
CS230's second lecture derives embeddings through three case studies: day/night classification teaches you to use humans as a proxy for choosing resolution, trigger-word detection teaches you to manufacture a million training examples in three hours, and face verification walks you through designing your first loss function. The final step — from supervised triplets to self-supervised pairs — is why modern models can consume billions of unlabeled images.