papersSEP 12 04:00 UTC
Logit Refiner Targets Intra-Scale Dependencies in Visual Autoregressive Models
A new arXiv paper analyzes visual autoregressive models, which generate images by predicting one scale of tokens at a time and emitting all tokens in a scale in parallel. The authors argue this parallel decoding acts as a mean-field-style approximation that drops spatial dependencies within each scale. They propose a Logit Refiner method that models these intra-scale relationships to improve generation quality.
arXivLogit Refinerintra-scale-dependenciesmean-field-approximationparallel decodingvisual-autoregressive-models
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arXiv cs.LGLogit Refiner: Improving Visual Autoregressive Models via Intra-Scale Dependency Modeling ↗SEP 11 04:00 UTC
arXiv cs.AILogit Refiner: Improving Visual Autoregressive Models via Intra-Scale Dependency Modeling ↗SEP 12 04:00 UTC