title: Neural Network Diffusion
summary: Diffusion models have achieved remarkable success in image and video
  generation. In this work, we demonstrate that diffusion models can also
  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.
paper_authors._id: 65d565d0aa212698b489e05a
paper_authors.name: Kai Wang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-02-21T12:03:09.955Z
paper_authors._id: 655452b8432af1b1116394d1
paper_authors.avatarUrl: /avatars/85860fb3c2d09c9c23e7677d7129cca3.svg
paper_authors.fullname: Kai Wang
paper_authors.type: user
paper_authors.user: VictorKai1996NUS
paper_authors._id: 65d565d0aa212698b489e05b
paper_authors.name: Zhaopan Xu
paper_authors._id: 65d565d0aa212698b489e05c
paper_authors.name: Yukun Zhou
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-02-21T12:03:12.352Z
paper_authors._id: 63ecdc6b54bba6781518cf48
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/63ecdc6b54bba6781518cf48/d5DrpcYnxjW0Z1BrjrftB.jpeg
paper_authors.fullname: YuKun Zhou
paper_authors.type: user
paper_authors.user: 1zeryu
paper_authors._id: 65d565d0aa212698b489e05d
paper_authors.name: Zelin Zang
paper_authors._id: 65d565d0aa212698b489e05e
paper_authors.name: Trevor Darrell
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-02-21T09:26:00.024Z
paper_authors._id: 64cbdf02f103036e23d1c7f3
paper_authors.avatarUrl: /avatars/496069463900dea20929b57381182d39.svg
paper_authors.fullname: Trevor Darrell
paper_authors.type: user
paper_authors.user: trevordarrell
paper_authors._id: 65d565d0aa212698b489e05f
paper_authors.name: Zhuang Liu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-02-21T19:37:55.778Z
paper_authors._id: 647d3a9383cdb93baf76dce2
paper_authors.avatarUrl: /avatars/56efca912a66ef9d4f2f3679aac31bae.svg
paper_authors.fullname: Zhuang Liu
paper_authors.type: user
paper_authors.user: liuzhuang13
paper_authors._id: 65d565d0aa212698b489e060
paper_authors.name: Yang You
paper_title: Neural Network Diffusion
paper_summary: Diffusion models have achieved remarkable success in image and video
  generation. In this work, we demonstrate that diffusion models can also
  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.
paper_ai_summary: Diffusion models can generate high-performing neural network parameters using an autoencoder, producing new subsets of network parameters with comparable or improved performance.
paper_ai_keywords: diffusion models autoencoder latent representations latent parameter representations decoder neural network parameters