title: ε-VAE: Denoising as Visual Decoding
summary: In generative modeling, tokenization simplifies complex data into compact,
  structured representations, creating a more efficient, learnable space. For
  high-dimensional visual data, it reduces redundancy and emphasizes key features
  for high-quality generation. Current visual tokenization methods rely on a
  traditional autoencoder framework, where the encoder compresses data into
  latent representations, and the decoder reconstructs the original input. In
  this work, we offer a new perspective by proposing denoising as decoding,
  shifting from single-step reconstruction to iterative refinement. Specifically,
  we replace the decoder with a diffusion process that iteratively refines noise
  to recover the original image, guided by the latents provided by the encoder.
  We evaluate our approach by assessing both reconstruction (rFID) and generation
  quality (FID), comparing it to state-of-the-art autoencoding approach. We hope
  this work offers new insights into integrating iterative generation and
  autoencoding for improved compression and generation.
paper_authors._id: 6706ec61981508d3eb268d4e
paper_authors.name: Long Zhao
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-10-10T08:01:55.932Z
paper_authors._id: 650c249887dcda6616baa040
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/tIVfxoAHAJ0sWyDMkaarA.jpeg
paper_authors.fullname: Long Zhao
paper_authors.type: user
paper_authors.user: garyzhao9012
paper_authors._id: 6706ec61981508d3eb268d4f
paper_authors.name: Sanghyun Woo
paper_authors._id: 6706ec61981508d3eb268d50
paper_authors.name: Ziyu Wan
paper_authors._id: 6706ec61981508d3eb268d51
paper_authors.name: Yandong Li
paper_authors._id: 6706ec61981508d3eb268d52
paper_authors.name: Han Zhang
paper_authors._id: 6706ec61981508d3eb268d53
paper_authors.name: Boqing Gong
paper_authors._id: 6706ec61981508d3eb268d54
paper_authors.name: Hartwig Adam
paper_authors._id: 6706ec61981508d3eb268d55
paper_authors.name: Xuhui Jia
paper_authors._id: 6706ec61981508d3eb268d56
paper_authors.name: Ting Liu
paper_title: ε-VAE: Denoising as Visual Decoding
paper_summary: In generative modeling, tokenization simplifies complex data into compact,
  structured representations, creating a more efficient, learnable space. For
  high-dimensional visual data, it reduces redundancy and emphasizes key features
  for high-quality generation. Current visual tokenization methods rely on a
  traditional autoencoder framework, where the encoder compresses data into
  latent representations, and the decoder reconstructs the original input. In
  this work, we offer a new perspective by proposing denoising as decoding,
  shifting from single-step reconstruction to iterative refinement. Specifically,
  we replace the decoder with a diffusion process that iteratively refines noise
  to recover the original image, guided by the latents provided by the encoder.
  We evaluate our approach by assessing both reconstruction (rFID) and generation
  quality (FID), comparing it to state-of-the-art autoencoding approach. We hope
  this work offers new insights into integrating iterative generation and
  autoencoding for improved compression and generation.
paper_ai_keywords: generative modeling tokenization latent representations autoencoder denoising diffusion process iterative refinement rFID FID