BiGR: Harnessing Binary Latent Codes for Image Generation and Improved Visual Representation Capabilities
Shaozhe Hao Xuantong LIU Xianbiao Qi Shihao Zhao Bojia Zi Rong Xiao Kai Han Kwan-Yee K. Wong
We introduce BiGR, a novel conditional image generation model using compact binary latent codes for generative training, focusing on enhancing both generation and representation capabilities. BiGR is the first conditional generative model that unifies generation and discrimination within the same framework. 
BiGR features a binary tokenizer, a masked modeling mechanism, and a binary transcoder for binary code prediction. 
Additionally, we introduce a novel entropy-ordered sampling method to enable efficient image generation. 
Extensive experiments validate BiGR's superior performance in generation quality, as measured by FID-50k, and representation capabilities, as evidenced by linear-probe accuracy. 
Moreover, BiGR showcases zero-shot generalization across various vision tasks, enabling applications such as image inpainting, outpainting, editing, interpolation, and enrichment, without the need for structural modifications. Our findings suggest that BiGR unifies generative and discriminative tasks effectively, paving the way for further advancements in the field. We further enable BiGR to perform text-to-image generation, showcasing its potential for broader applications.
Image generation, Generative model, Representation learning
We present a novel conditional image generation model that unifies generative and discriminative tasks effectively.