title: Aligning Latent Spaces with Flow Priors
summary: This paper presents a novel framework for aligning learnable latent spaces to
  arbitrary target distributions by leveraging flow-based generative models as
  priors. Our method first pretrains a flow model on the target features to
  capture the underlying distribution. This fixed flow model subsequently
  regularizes the latent space via an alignment loss, which reformulates the flow
  matching objective to treat the latents as optimization targets. We formally
  prove that minimizing this alignment loss establishes a computationally
  tractable surrogate objective for maximizing a variational lower bound on the
  log-likelihood of latents under the target distribution. Notably, the proposed
  method eliminates computationally expensive likelihood evaluations and avoids
  ODE solving during optimization. As a proof of concept, we demonstrate in a
  controlled setting that the alignment loss landscape closely approximates the
  negative log-likelihood of the target distribution. We further validate the
  effectiveness of our approach through large-scale image generation experiments
  on ImageNet with diverse target distributions, accompanied by detailed
  discussions and ablation studies. With both theoretical and empirical
  validation, our framework paves a new way for latent space alignment.
paper_authors._id: 684249e23fb0b2ecb854594a
paper_authors.name: Yizhuo Li
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-06-06T07:41:23.836Z
paper_authors._id: 630b094f8b327c7b8b94d24c
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/630b094f8b327c7b8b94d24c/Fd0ugLOHJZIi8oUZScOmX.jpeg
paper_authors.fullname: Yizhuo Li
paper_authors.type: user
paper_authors.user: liyz
paper_authors._id: 684249e23fb0b2ecb854594b
paper_authors.name: Yuying Ge
paper_authors._id: 684249e23fb0b2ecb854594c
paper_authors.name: Yixiao Ge
paper_authors._id: 684249e23fb0b2ecb854594d
paper_authors.name: Ying Shan
paper_authors._id: 684249e23fb0b2ecb854594e
paper_authors.name: Ping Luo
paper_title: Aligning Latent Spaces with Flow Priors
paper_summary: This paper presents a novel framework for aligning learnable latent spaces to
  arbitrary target distributions by leveraging flow-based generative models as
  priors. Our method first pretrains a flow model on the target features to
  capture the underlying distribution. This fixed flow model subsequently
  regularizes the latent space via an alignment loss, which reformulates the flow
  matching objective to treat the latents as optimization targets. We formally
  prove that minimizing this alignment loss establishes a computationally
  tractable surrogate objective for maximizing a variational lower bound on the
  log-likelihood of latents under the target distribution. Notably, the proposed
  method eliminates computationally expensive likelihood evaluations and avoids
  ODE solving during optimization. As a proof of concept, we demonstrate in a
  controlled setting that the alignment loss landscape closely approximates the
  negative log-likelihood of the target distribution. We further validate the
  effectiveness of our approach through large-scale image generation experiments
  on ImageNet with diverse target distributions, accompanied by detailed
  discussions and ablation studies. With both theoretical and empirical
  validation, our framework paves a new way for latent space alignment.
paper_ai_summary: A novel framework using flow-based generative models aligns learnable latent spaces to target distributions, reducing computational expense and improving log-likelihood maximization.
paper_ai_keywords: flow-based generative models latent spaces alignment loss flow matching objective variational lower bound log-likelihood ImageNet