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arXiv Machine Learning By Alan Baade, Kyle Sargent, Vincent Sitzmann, Eric Ryan Chan, Justin Johnson, Li Fei-Fei, Gordon Wetzstein

Generative Representation Learning for Scalable Latent Tokenization

Read the original on arXiv Machine Learning →

The paper proposes a self‑supervised representation learning method that serves as a tokenizer for generative models. It supervises a representation loss by factorizing pointwise mutual information, estimated through differences between conditional predictions of a learned generative model. The authors demonstrate that this approach avoids dimensional collapse and feature suppression seen in methods like DINO and LeJEPA, scales predictably with compute, and achieves compute‑optimal performance over direct diffusion for pixel‑space generation, with strong scaling laws on ImageNet and GPIC.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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