title: Continuous Visual Autoregressive Generation via Score Maximization
summary: Conventional wisdom suggests that autoregressive models are used to process
  discrete data. When applied to continuous modalities such as visual data,
  Visual AutoRegressive modeling (VAR) typically resorts to quantization-based
  approaches to cast the data into a discrete space, which can introduce
  significant information loss. To tackle this issue, we introduce a Continuous
  VAR framework that enables direct visual autoregressive generation without
  vector quantization. The underlying theoretical foundation is strictly proper
  scoring rules, which provide powerful statistical tools capable of evaluating
  how well a generative model approximates the true distribution. Within this
  framework, all we need is to select a strictly proper score and set it as the
  training objective to optimize. We primarily explore a class of training
  objectives based on the energy score, which is likelihood-free and thus
  overcomes the difficulty of making probabilistic predictions in the continuous
  space. Previous efforts on continuous autoregressive generation, such as GIVT
  and diffusion loss, can also be derived from our framework using other strictly
  proper scores. Source code: https://github.com/shaochenze/EAR.
paper_authors._id: 68230f352dcd4033dcf9d1a5
paper_authors.name: Chenze Shao
paper_authors.status: extracted_pending
paper_authors.statusLastChangedAt: 2025-05-13T09:21:58.755Z
paper_authors._id: 67a42bf8dba32bb665e351ad
paper_authors.avatarUrl: /avatars/9c13810fe789ddcd9cefd4f2c924e4aa.svg
paper_authors.fullname: Chenze Shao
paper_authors.type: user
paper_authors.user: cccczshao
paper_authors._id: 68230f352dcd4033dcf9d1a6
paper_authors.name: Fandong Meng
paper_authors._id: 68230f352dcd4033dcf9d1a7
paper_authors.name: Jie Zhou
paper_title: Continuous Visual Autoregressive Generation via Score Maximization
paper_summary: Conventional wisdom suggests that autoregressive models are used to process
  discrete data. When applied to continuous modalities such as visual data,
  Visual AutoRegressive modeling (VAR) typically resorts to quantization-based
  approaches to cast the data into a discrete space, which can introduce
  significant information loss. To tackle this issue, we introduce a Continuous
  VAR framework that enables direct visual autoregressive generation without
  vector quantization. The underlying theoretical foundation is strictly proper
  scoring rules, which provide powerful statistical tools capable of evaluating
  how well a generative model approximates the true distribution. Within this
  framework, all we need is to select a strictly proper score and set it as the
  training objective to optimize. We primarily explore a class of training
  objectives based on the energy score, which is likelihood-free and thus
  overcomes the difficulty of making probabilistic predictions in the continuous
  space. Previous efforts on continuous autoregressive generation, such as GIVT
  and diffusion loss, can also be derived from our framework using other strictly
  proper scores. Source code: https://github.com/shaochenze/EAR.
paper_ai_summary: A Continuous VAR framework facilitates direct autoregressive generation of continuous visual data without quantization, using strictly proper scoring rules for optimization.
paper_ai_keywords: Visual AutoRegressive modeling VAR continuous VAR strictly proper scoring rules energy score GIVT diffusion loss