让神经网络像橡皮泥一样随意变形，还能保持精度。
How to Train Your Metamorphic Deep Neural Network
- 分块渐进训练+INR初始化，实现全网结构可变
- 压缩比范围内准确率下降小于1%，支持任意宽度深度组合
- 适合需要灵活部署的边缘设备场景
神经元形态（NeuMeta）是一种生成可变宽度和深度神经网络的新范式。基于隐式神经表示（INR），NeuMeta 学习连续权重流形，可直接生成压缩模型，包括训练时未见的配置。然而，原始方法仅对模型末层有效，限制了应用范围。本文提出一种训练算法，将 NeuMeta 扩展至全网络，实现最小精度损失的全面形态变化。方法包含分块渐进训练、INR 初始化及批归一化替换策略。生成的可变形网络在多种压缩比下保持高准确率，为深度模型的可扩展、高效部署提供方案。代码已开源：https://github.com/TSommariva/HTTY_NeuMeta。
原文摘要 · Abstract (English)
Neural Metamorphosis (NeuMeta) is a recent paradigm for generating neural networks of varying width and depth. Based on Implicit Neural Representation (INR), NeuMeta learns a continuous weight manifold, enabling the direct generation of compressed models, including those with configurations not seen during training. While promising, the original formulation of NeuMeta proves effective only for the final layers of the undelying model, limiting its broader applicability. In this work, we propose a training algorithm that extends the capabilities of NeuMeta to enable full-network metamorphosis with minimal accuracy degradation. Our approach follows a structured recipe comprising block-wise incremental training, INR initialization, and strategies for replacing batch normalization. The resulting metamorphic networks maintain competitive accuracy across a wide range of compression ratios, offering a scalable solution for adaptable and efficient deployment of deep models. The code is available at: https://github.com/TSommariva/HTTY_NeuMeta.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。