Revisiting Autoregressive Models for Generative Image Classification
Class-conditional generative models have emerged as accurate and robust classifiers, with diffusion models demonstrating clear advantages over other visual generative paradigms, including autoregressive (AR) models. In this work, we revisit visual AR-based generative classifiers and identify an important limitation of prior approaches: their reliance on a fixed token order, which imposes a restrictive inductive bias for image understanding. We observe that single-order predictions rely more on partial discriminative cues, while averaging over multiple token orders provides a more comprehensive signal. Based on this insight, we leverage recent any-order AR models to estimate order-marginal...
| Authors | Ilia Sudakov, Artem Babenko, Dmitry Baranchuk |
|---|---|
| Primary category | cs.CV |
| Published | 19 March 2026 |
| Last updated at source | 1 July 2026 |
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