Block Flow: Learning Straight Flow on Data Blocks

• URL: http://arxiv.org/abs/2501.11361v1
• Date: Mon, 20 Jan 2025 09:46:12 GMT
• Title: Block Flow: Learning Straight Flow on Data Blocks
• Authors: Zibin Wang, Zhiyuan Ouyang, Xiangyun Zhang,
• Abstract summary: Flow-matching models are characterized by flows with low curvature in learned generative trajectories. We propose block matching to further reduce curvature. We demonstrate that the variance of the prior distribution can control the curvature upper bound of forward trajectories.
• Score: 0.0
• License:
• Abstract: Flow-matching models provide a powerful framework for various applications, offering efficient sampling and flexible probability path modeling. These models are characterized by flows with low curvature in learned generative trajectories, which results in reduced truncation error at each sampling step. To further reduce curvature, we propose block matching. This novel approach leverages label information to partition the data distribution into blocks and match them with a prior distribution parameterized using the same label information, thereby learning straighter flows. We demonstrate that the variance of the prior distribution can control the curvature upper bound of forward trajectories in flow-matching models. By designing flexible regularization strategies to adjust this variance, we achieve optimal generation performance, effectively balancing the trade-off between maintaining diversity in generated samples and minimizing numerical solver errors. Our results demonstrate competitive performance with models of the same parameter scale.Code is available at \url{https://github.com/wpp13749/block_flow}.

Related papers

• Local Flow Matching Generative Models [19.859984725284896]
  Local Flow Matching is a computational framework for density estimation based on flow-based generative models. $textttLFM$ employs a simulation-free scheme and incrementally learns a sequence of Flow Matching sub-models. We demonstrate the improved training efficiency and competitive generative performance of $textttLFM$ compared to FM.
  arXiv  Detail & Related papers  (2024-10-03T14:53:10Z)
• Discrete Flow Matching [74.04153927689313]
  We present a novel discrete flow paradigm designed specifically for generating discrete data. Our approach is capable of generating high-quality discrete data in a non-autoregressive fashion.
  arXiv  Detail & Related papers  (2024-07-22T12:33:27Z)
• Guided Flows for Generative Modeling and Decision Making [55.42634941614435]
  We show that Guided Flows significantly improves the sample quality in conditional image generation and zero-shot text synthesis-to-speech. Notably, we are first to apply flow models for plan generation in the offline reinforcement learning setting ax speedup in compared to diffusion models.
  arXiv  Detail & Related papers  (2023-11-22T15:07:59Z)
• VQ-Flows: Vector Quantized Local Normalizing Flows [2.7998963147546148]
  We introduce a novel statistical framework for learning a mixture of local normalizing flows as "chart maps" over a data manifold. Our framework augments the expressivity of recent approaches while preserving the signature property of normalizing flows, that they admit exact density evaluation.
  arXiv  Detail & Related papers  (2022-03-22T09:22:18Z)
• Discrete Denoising Flows [87.44537620217673]
  We introduce a new discrete flow-based model for categorical random variables: Discrete Denoising Flows (DDFs) In contrast with other discrete flow-based models, our model can be locally trained without introducing gradient bias. We show that DDFs outperform Discrete Flows on modeling a toy example, binary MNIST and Cityscapes segmentation maps, measured in log-likelihood.
  arXiv  Detail & Related papers  (2021-07-24T14:47:22Z)
• DeFlow: Learning Complex Image Degradations from Unpaired Data with Conditional Flows [145.83812019515818]
  We propose DeFlow, a method for learning image degradations from unpaired data. We model the degradation process in the latent space of a shared flow-decoder network. We validate our DeFlow formulation on the task of joint image restoration and super-resolution.
  arXiv  Detail & Related papers  (2021-01-14T18:58:01Z)
• Haar Wavelet based Block Autoregressive Flows for Trajectories [129.37479472754083]
  Prediction of trajectories such as that of pedestrians is crucial to the performance of autonomous agents. We introduce a novel Haar wavelet based block autoregressive model leveraging split couplings. We illustrate the advantages of our approach for generating diverse and accurate trajectories on two real-world datasets.
  arXiv  Detail & Related papers  (2020-09-21T13:57:10Z)
• Variational Mixture of Normalizing Flows [0.0]
  Deep generative models, such as generative adversarial networks autociteGAN, variational autoencoders autocitevaepaper, and their variants, have seen wide adoption for the task of modelling complex data distributions. Normalizing flows have overcome this limitation by leveraging the change-of-suchs formula for probability density functions. The present work overcomes this by using normalizing flows as components in a mixture model and devising an end-to-end training procedure for such a model.
  arXiv  Detail & Related papers  (2020-09-01T17:20:08Z)
• SoftFlow: Probabilistic Framework for Normalizing Flow on Manifolds [15.476426879806134]
  Flow-based generative models are composed of invertible transformations between two random variables of the same dimension. In this paper, we propose SoftFlow, a probabilistic framework for training normalizing flows on manifold. We experimentally show that SoftFlow can capture the innate structure of the manifold data and generate high-quality samples. We apply the proposed framework to 3D point clouds to alleviate the difficulty of forming thin structures for flow-based models.
  arXiv  Detail & Related papers  (2020-06-08T13:56:07Z)
• Semi-Supervised Learning with Normalizing Flows [54.376602201489995]
  FlowGMM is an end-to-end approach to generative semi supervised learning with normalizing flows. We show promising results on a wide range of applications, including AG-News and Yahoo Answers text data.
  arXiv  Detail & Related papers  (2019-12-30T17:36:33Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.