title: BiGR: Harnessing Binary Latent Codes for Image Generation and Improved
    Visual Representation Capabilities
summary: 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.
paper_authors._id: 6715b0bc7dfe714b42e6fc6c
paper_authors.name: Shaozhe Hao
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-10-21T16:13:37.342Z
paper_authors._id: 63b67ab2877d94d5c04946e1
paper_authors.avatarUrl: /avatars/56863b04863969b9aaa35194e25d1e82.svg
paper_authors.fullname: Shaozhe Hao
paper_authors.type: user
paper_authors.user: haoosz
paper_authors._id: 6715b0bc7dfe714b42e6fc6d
paper_authors.name: Xuantong Liu
paper_authors._id: 6715b0bc7dfe714b42e6fc6e
paper_authors.name: Xianbiao Qi
paper_authors.status: extracted_pending
paper_authors.statusLastChangedAt: 2024-10-21T01:39:11.134Z
paper_authors._id: 6494483aa13255720397287a
paper_authors.avatarUrl: /avatars/61ff2e0371df513194246cf6fbb2b78a.svg
paper_authors.fullname: Xianbiao Qi
paper_authors.type: user
paper_authors.user: qixianbiao
paper_authors._id: 6715b0bc7dfe714b42e6fc6f
paper_authors.name: Shihao Zhao
paper_authors._id: 6715b0bc7dfe714b42e6fc70
paper_authors.name: Bojia Zi
paper_authors._id: 6715b0bc7dfe714b42e6fc71
paper_authors.name: Rong Xiao
paper_authors._id: 6715b0bc7dfe714b42e6fc72
paper_authors.name: Kai Han
paper_authors._id: 6715b0bc7dfe714b42e6fc73
paper_authors.name: Kwan-Yee K. Wong
paper_title: BiGR: Harnessing Binary Latent Codes for Image Generation and Improved
    Visual Representation Capabilities
paper_summary: 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.
paper_ai_keywords: BiGR conditional image generation model compact binary latent codes generative training binary tokenizer masked modeling mechanism binary transcoder binary code prediction entropy-ordered sampling method FID-50k linear-probe accuracy zero-shot generalization image inpainting outpainting image editing image interpolation image enrichment generative task discriminative task