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Computer Science > Machine Learning

arXiv:2110.07579 (cs)
[Submitted on 14 Oct 2021]

Title:Diffusion Normalizing Flow

Authors:Qinsheng Zhang, Yongxin Chen
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    Abstract: We present a novel generative modeling method called
    diffusion normalizing flow based on stochastic differential
    equations (SDEs). The algorithm consists of two neural SDEs: a
    forward SDE that gradually adds noise to the data to transform
    the data into Gaussian random noise, and a backward SDE that
    gradually removes the noise to sample from the data distribution.
    By jointly training the two neural SDEs to minimize a common cost
    function that quantifies the difference between the two, the
    backward SDE converges to a diffusion process the starts with a
    Gaussian distribution and ends with the desired data
    distribution. Our method is closely related to normalizing flow
    and diffusion probabilistic models and can be viewed as a
    combination of the two. Compared with normalizing flow, diffusion
    normalizing flow is able to learn distributions with sharp
    boundaries. Compared with diffusion probabilistic models,
    diffusion normalizing flow requires fewer discretization steps
    and thus has better sampling efficiency. Our algorithm
    demonstrates competitive performance in both high-dimension data
    density estimation and image generation tasks.

Comments: Neurips 2021
Subjects: Machine Learning (cs.LG)
Cite as:  arXiv:2110.07579 [cs.LG]
          (or arXiv:2110.07579v1 [cs.LG] for this version)

Submission history

From: Qinsheng Zhang [view email]
[v1] Thu, 14 Oct 2021 17:41:12 UTC (27,738 KB)
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