Neural Metamorphosis
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.
6710b99b3ff472e2be83ba05
Xingyi Yang
claimed_verified
2025-06-05T07:30:53.148Z
634cfebc350bcee9bed20a4d
https://cdn-avatars.huggingface.co/v1/production/uploads/634cfebc350bcee9bed20a4d/fN47nN5rhw-HJaFLBZWQy.png
Xingyi Yang
user
adamdad
6710b99b3ff472e2be83ba06
Xinchao Wang
Neural Metamorphosis
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.
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.
Neural Metamorphosis NeuMeta self-morphable neural networks continuous weight manifold hypernetworks neural implicit functions model space smoothness Shortest Hamiltonian Path problem intra-model smoothness