Summary of Neural Metamorphosis, by Xingyi Yang et al.
Neural Metamorphosis
by Xingyi Yang, Xinchao Wang
First submitted to arxiv on: 10 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper introduces Neural Metamorphosis (NeuMeta), a novel learning paradigm that enables self-morphic neural networks. Unlike traditional approaches, which require separate models for different architectures or sizes, NeuMeta directly learns the continuous weight manifold of neural networks. This allows for sampling weights for any-sized network without retraining, even for previously unseen configurations. To achieve this, NeuMeta trains neural implicit functions as hypernetworks, accepting coordinates within the model space and generating corresponding weight values on the manifold. The paper highlights that the smoothness of the learned manifold significantly impacts final performance, and employs two strategies to enhance it: permuting weight matrices to ensure intra-model smoothness and adding noise to input coordinates for consistent outputs. Experimental results in image classification, semantic segmentation, and image generation demonstrate NeuMeta’s ability to sustain full-size performance even at a 75% compression rate. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary NeuMeta is a new way of building neural networks that can change shape depending on the task. Instead of making separate models for different sizes or types of networks, NeuMeta learns how to create any-sized network from scratch. This is achieved by training special functions called hypernetworks that can generate weights for any-sized network. The paper shows that this approach works well in image classification, semantic segmentation, and image generation tasks, even when the network is reduced to 25% of its original size. |
Keywords
» Artificial intelligence » Image classification » Image generation » Semantic segmentation
