BiGR: Harnessing Binary Latent Codes for Image Generation and Improved
  Visual Representation Capabilities
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.
6715b0bc7dfe714b42e6fc6c
Shaozhe Hao
claimed_verified
2024-10-21T16:13:37.342Z
63b67ab2877d94d5c04946e1
/avatars/56863b04863969b9aaa35194e25d1e82.svg
Shaozhe Hao
user
haoosz
6715b0bc7dfe714b42e6fc6d
Xuantong Liu
6715b0bc7dfe714b42e6fc6e
Xianbiao Qi
extracted_pending
2024-10-21T01:39:11.134Z
6494483aa13255720397287a
/avatars/61ff2e0371df513194246cf6fbb2b78a.svg
Xianbiao Qi
user
qixianbiao
6715b0bc7dfe714b42e6fc6f
Shihao Zhao
6715b0bc7dfe714b42e6fc70
Bojia Zi
6715b0bc7dfe714b42e6fc71
Rong Xiao
6715b0bc7dfe714b42e6fc72
Kai Han
6715b0bc7dfe714b42e6fc73
Kwan-Yee K. Wong
BiGR: Harnessing Binary Latent Codes for Image Generation and Improved
  Visual Representation Capabilities
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.
BiGR is a binary latent code-based conditional image generation model that unifies generation and discrimination, achieving high-quality image generation and versatile zero-shot generalization across vision tasks.
BiGR binary latent codes conditional generative model unification binary tokenizer masked modeling mechanism binary transcoder binary code prediction entropy-ordered sampling FID-50k linear-probe accuracy image inpainting outpainting image editing image interpolation image enrichment