title: Neural Metamorphosis
summary: This paper introduces a new learning paradigm termed Neural Metamorphosis
  (NeuMeta), which aims to build self-morphable neural networks. Contrary to
  crafting separate models for different architectures or sizes, NeuMeta directly
  learns the continuous weight manifold of neural networks. Once trained, we can
  sample weights for any-sized network directly from the manifold, even for
  previously unseen configurations, without retraining. To achieve this ambitious
  goal, NeuMeta trains neural implicit functions as hypernetworks. They accept
  coordinates within the model space as input, and generate corresponding weight
  values on the manifold. In other words, the implicit function is learned in a
  way, that the predicted weights is well-performed across various models sizes.
  In training those models, we notice that, the final performance closely relates
  on smoothness of the learned manifold. In pursuit of enhancing this smoothness,
  we employ two strategies. First, we permute weight matrices to achieve
  intra-model smoothness, by solving the Shortest Hamiltonian Path problem.
  Besides, we add a noise on the input coordinates when training the implicit
  function, ensuring models with various sizes shows consistent outputs. As such,
  NeuMeta shows promising results in synthesizing parameters for various network
  configurations. Our extensive tests in image classification, semantic
  segmentation, and image generation reveal that NeuMeta sustains full-size
  performance even at a 75% compression rate.
paper_authors._id: 6710b99b3ff472e2be83ba05
paper_authors.name: Xingyi Yang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-06-05T07:30:53.148Z
paper_authors._id: 634cfebc350bcee9bed20a4d
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/634cfebc350bcee9bed20a4d/fN47nN5rhw-HJaFLBZWQy.png
paper_authors.fullname: Xingyi Yang
paper_authors.type: user
paper_authors.user: adamdad
paper_authors._id: 6710b99b3ff472e2be83ba06
paper_authors.name: Xinchao Wang
paper_title: Neural Metamorphosis
paper_summary: This paper introduces a new learning paradigm termed Neural Metamorphosis
  (NeuMeta), which aims to build self-morphable neural networks. Contrary to
  crafting separate models for different architectures or sizes, NeuMeta directly
  learns the continuous weight manifold of neural networks. Once trained, we can
  sample weights for any-sized network directly from the manifold, even for
  previously unseen configurations, without retraining. To achieve this ambitious
  goal, NeuMeta trains neural implicit functions as hypernetworks. They accept
  coordinates within the model space as input, and generate corresponding weight
  values on the manifold. In other words, the implicit function is learned in a
  way, that the predicted weights is well-performed across various models sizes.
  In training those models, we notice that, the final performance closely relates
  on smoothness of the learned manifold. In pursuit of enhancing this smoothness,
  we employ two strategies. First, we permute weight matrices to achieve
  intra-model smoothness, by solving the Shortest Hamiltonian Path problem.
  Besides, we add a noise on the input coordinates when training the implicit
  function, ensuring models with various sizes shows consistent outputs. As such,
  NeuMeta shows promising results in synthesizing parameters for various network
  configurations. Our extensive tests in image classification, semantic
  segmentation, and image generation reveal that NeuMeta sustains full-size
  performance even at a 75% compression rate.
paper_ai_summary: Neural Metamorphosis learns a continuous weight manifold for neural networks, enabling sampling of weights for any configuration without retraining by using neural implicit functions as hypernetworks.
paper_ai_keywords: Neural Metamorphosis NeuMeta self-morphable neural networks continuous weight manifold hypernetworks neural implicit functions model space smoothness Shortest Hamiltonian Path problem intra-model smoothness