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Computer Science > Machine Learning

arXiv:2402.13144 (cs)
[Submitted on 20 Feb 2024]

Title:Neural Network Diffusion

Authors:Kai Wang, Zhaopan Xu, Yukun Zhou, Zelin Zang, Trevor Darrell,
Zhuang Liu, Yang You
Download a PDF of the paper titled Neural Network Diffusion, by Kai
Wang and 6 other authors
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    Abstract:Diffusion models have achieved remarkable success in
    image and video generation. In this work, we demonstrate that
    diffusion models can also \textit{generate high-performing neural
    network parameters}. Our approach is simple, utilizing an
    autoencoder and a standard latent diffusion model. The
    autoencoder extracts latent representations of a subset of the
    trained network parameters. A diffusion model is then trained to
    synthesize these latent parameter representations from random
    noise. It then generates new representations that are passed
    through the autoencoder's decoder, whose outputs are ready to use
    as new subsets of network parameters. Across various
    architectures and datasets, our diffusion process consistently
    generates models of comparable or improved performance over
    trained networks, with minimal additional cost. Notably, we
    empirically find that the generated models perform differently
    with the trained networks. Our results encourage more exploration
    on the versatile use of diffusion models.

          We introduce a novel approach for parameter generation,
Comments: named neural network diffusion (\textbf{p-diff}, p stands
          for parameter), which employs a standard latent diffusion
          model to synthesize a new set of parameters
Subjects: Machine Learning (cs.LG); Computer Vision and Pattern
          Recognition (cs.CV)
Cite as:  arXiv:2402.13144 [cs.LG]
          (or arXiv:2402.13144v1 [cs.LG] for this version)
          https://doi.org/10.48550/arXiv.2402.13144
          Focus to learn more
          arXiv-issued DOI via DataCite

Submission history

From: Kai Wang [view email]
[v1] Tue, 20 Feb 2024 16:59:03 UTC (6,066 KB)
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