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cs.LG2026
Generative Representation Learning for Scalable Latent Tokenization
Alan Baade, Kyle Sargent, Vincent Sitzmann +4
We introduce an approach for self-supervised representation learning designed to be used as a tokenizer for generative modeling. Our objective supervises a representation loss by f…
cs.LG2025
RCPN: towards robust pseudo-labels in unsupervised CLIP adaptation via consistent prototypes and neighborhoods
Eman Ali, Chetan Arora, Muhammad Haris Khan
In unsupervised adaptation for vision-language models such as CLIP, pseudo-labels derived from zero-shot predictions often exhibit significant noise, particularly under domain shif…