Stanford paper: generative representation learning cuts tokenizer training compute by 7-8x
HirokatuKataoka · x · 2026-10-16
A Stanford/MIT/Michigan paper advised by Li Fei-Fei and Gordon Wetzstein introduces a self-supervised representation learning method designed as a tokenizer for generative modeling, supervising representation loss via pointwise mutual information factorization estimated with a learned generative model. It avoids the dimensional collapse and feature suppression seen with DINO and LeJEPA, improves ImageNet FID from 2.36 to 2.18 (JiT-L) at similar compute, and scaling-law extrapolations suggest 7x fewer FLOPs than DINOv2 and 8x fewer than WebSSL for tokenizer training. Code is open-sourced.
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