Understand Before You Generate: Self-Guided Training for Autoregressive
  Image Generation
Authors: name: Xiaoyu Yue, name: Zidong Wang, name: Yuqing Wang, name: Wenlong Zhang, name: Xihui Liu, name: Wanli Ouyang, name: Lei Bai, name: Luping Zhou

Recent studies have demonstrated the importance of high-quality visual
representations in image generation and have highlighted the limitations of
generative models in image understanding. As a generative paradigm originally
designed for natural language, autoregressive models face similar challenges.
In this work, we present the first systematic investigation into the mechanisms
of applying the next-token prediction paradigm to the visual domain. We
identify three key properties that hinder the learning of high-level visual
semantics: local and conditional dependence, inter-step semantic inconsistency,
and spatial invariance deficiency. We show that these issues can be effectively
addressed by introducing self-supervised objectives during training, leading to
a novel training framework, Self-guided Training for AutoRegressive models
(ST-AR). Without relying on pre-trained representation models, ST-AR
significantly enhances the image understanding ability of autoregressive models
and leads to improved generation quality. Specifically, ST-AR brings
approximately 42% FID improvement for LlamaGen-L and 49% FID improvement for
LlamaGen-XL, while maintaining the same sampling strategy.