Title: Your VAR Model is Secretly an Efficient and Explainable Generative Classifier
Primary area: generative models
Keywords: generative classifier, generative model, autoregressive model

Abstract: Generative classifiers, which leverage conditional generative models for classification, have recently demonstrated desirable properties such as robustness to distribution shifts. However, recent progress in this area has been largely driven by diffusion-based models, whose substantial computational cost severely limits scalability. This exclusive focus on diffusion-based methods has also constrained our broader understanding of generative classifiers.
In this work, we propose a novel generative classifier built on recent advances in visual autoregressive (VAR) modeling, which offers a new perspective for studying generative classifiers. To further enhance its performance, we introduce the Adaptive VAR Classifier$^+$ (A-VARC$^+$), which achieves a superior trade-off between accuracy and inference speed, thereby significantly improving practical applicability.
Moreover, we show that the VAR classifier exhibits fundamentally different properties from diffusion-based methods. In particular, due to its tractable likelihood, the VAR classifier enables visual explainability via token-wise mutual information and demonstrates inherent resistance to catastrophic forgetting in class-incremental learning tasks.

TL;DR: Your VAR Model is Secretly an Efficient and Explainable Generative Classifier