Summary of Adversarial Training Can Provably Improve Robustness: Theoretical Analysis Of Feature Learning Process Under Structured Data, by Binghui Li et al.
Adversarial Training Can Provably Improve Robustness: Theoretical Analysis of Feature Learning Process Under Structured Data
by Binghui Li, Yuanzhi Li
First submitted to arxiv on: 11 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 The paper explores the theoretical underpinnings of adversarial training in deep neural networks, a widely-used approach to improve model robustness against perturbations. Specifically, it examines why adversarial examples exist and how adversarial training methods enhance model robustness from the perspective of feature learning theory. In a multiple classification setting, the paper identifies two types of features: robust, sparse features resistant to perturbation, and non-robust, dense features susceptible to perturbation. The authors train a two-layer smoothed ReLU convolutional neural network on structured data, demonstrating that standard training leads to the learning of non-robust features, generating adversarial examples aligned with negative feature directions. They then investigate the gradient-based adversarial training algorithm, showing it can provably strengthen robust feature learning and suppress non-robust feature learning, improving model robustness. Empirical validation is provided on real-image datasets, including MNIST, CIFAR10, and SVHN. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper tries to understand why deep neural networks are easily fooled by fake examples (called adversarial examples) and how to make them more reliable. The authors look at a special kind of training called adversarial training that helps models be less affected by these fake examples. They show that when we train models normally, they tend to learn from features that are easy to manipulate, which creates the fake examples. To fix this, they use an algorithm that finds and uses these fake examples to make the model better at recognizing real ones. This helps the model become more robust, or resistant, to these fake examples. The authors test their ideas on real-world image datasets like MNIST, CIFAR10, and SVHN. |
Keywords
» Artificial intelligence » Classification » Neural network » Relu
