Summary of Theoretical Understanding Of Learning From Adversarial Perturbations, by Soichiro Kumano et al.
Theoretical Understanding of Learning from Adversarial Perturbations
by Soichiro Kumano, Hiroshi Kera, Toshihiko Yamasaki
First submitted to arxiv on: 16 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (stat.ML)
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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 abstract presents a theoretical framework for understanding how neural networks learn from adversarial perturbations. The study explores why these perturbations, which appear as noises, contain class features that enable generalization to correctly labeled test samples. The results show that various adversarial perturbations, even those affecting only a few pixels, contain sufficient class features for generalization. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Adversarial examples can trick neural networks and transfer between different networks, but we don’t fully understand why this happens. Some researchers think that the “noise” in these examples actually contains important information about what’s being classified. This makes sense because if a network is trained on noisy versions of images or text, it can still learn to recognize the real things well. But we need a deeper understanding of how this works. In this study, scientists developed a way to explain how neural networks learn from these “noisy” examples using a simple kind of network and special training samples. |
Keywords
* Artificial intelligence * Generalization
