Neural Network Diffusion
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.
65d565d0aa212698b489e05a
Kai Wang
claimed_verified
2024-02-21T12:03:09.955Z
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Kai Wang
user
VictorKai1996NUS
65d565d0aa212698b489e05b
Zhaopan Xu
65d565d0aa212698b489e05c
Yukun Zhou
claimed_verified
2024-02-21T12:03:12.352Z
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YuKun Zhou
user
1zeryu
65d565d0aa212698b489e05d
Zelin Zang
65d565d0aa212698b489e05e
Trevor Darrell
admin_assigned
2024-02-21T09:26:00.024Z
64cbdf02f103036e23d1c7f3
/avatars/496069463900dea20929b57381182d39.svg
Trevor Darrell
user
trevordarrell
65d565d0aa212698b489e05f
Zhuang Liu
claimed_verified
2024-02-21T19:37:55.778Z
647d3a9383cdb93baf76dce2
/avatars/56efca912a66ef9d4f2f3679aac31bae.svg
Zhuang Liu
user
liuzhuang13
65d565d0aa212698b489e060
Yang You
Neural Network Diffusion
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.
Diffusion models can generate high-performing neural network parameters using an autoencoder, producing new subsets of network parameters with comparable or improved performance.
diffusion models autoencoder latent representations latent parameter representations decoder neural network parameters