New paper advised by Li Fei-Fei! "Generative Representation Learning for Scalable Latent Tokenization" Image generation commonly uses pretrained encoders like DINO as tokenizers, but these can discard visual details important for generation. So this paper trains encoders to preserve information shared between masked image views, using a generative model to guide learning. On ImageNet, it improves FID from 2.36 to 2.18 at similar total training compute, with scaling-law extrapolations…