Summary of Chain: Enhancing Generalization in Data-efficient Gans Via Lipschitz Continuity Constrained Normalization, by Yao Ni et al.
CHAIN: Enhancing Generalization in Data-Efficient GANs via lipsCHitz continuity constrAIned Normalization
by Yao Ni, Piotr Koniusz
First submitted to arxiv on: 31 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
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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 Generative Adversarial Networks (GANs) have revolutionized image generation, but their performance relies heavily on abundant training data. When facing limited data scenarios, GANs often struggle with discriminator overfitting and unstable training. This paper addresses the gap in using Batch Normalization (BN) in Data-Efficient GANs by identifying a critical flaw in BN’s gradient explosion during centering and scaling steps. The proposed CHAIN (lipsCHitz continuity constrAIned Normalization) tackles this issue by replacing the conventional centering step with zero-mean regularization, integrating Lipschitz continuity constraint in the scaling step, and adaptively interpolating normalized and unnormalized features to avoid discriminator overfitting. Theoretical analyses demonstrate CHAIN’s effectiveness in reducing gradients in latent features and weights, improving stability and generalization in GAN training. Empirical evidence supports this theory, achieving state-of-the-art results on CIFAR-10/100, ImageNet, five low-shot, and seven high-resolution few-shot image datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper improves how computers generate new images using a technique called Generative Adversarial Networks (GANs). GANs are great at making realistic pictures, but they need lots of training data to work well. When there’s not enough data, GANs can get stuck or make bad pictures. The authors found a problem in a tool called Batch Normalization that helps GANs train better. They created a new way to use this tool, called CHAIN, which makes GANs more stable and accurate when working with limited data. This is important because it means we can use GANs to create new images from smaller datasets. The authors tested their method on several image datasets and found that it performed better than other methods. |
Keywords
» Artificial intelligence » Batch normalization » Few shot » Gan » Generalization » Image generation » Overfitting » Regularization
