Summary of Maintaining Adversarial Robustness in Continuous Learning, by Xiaolei Ru et al.
Maintaining Adversarial Robustness in Continuous Learning
by Xiaolei Ru, Xiaowei Cao, Zijia Liu, Jack Murdoch Moore, Xin-Ya Zhang, Xia Zhu, Wenjia Wei, Gang Yan
First submitted to arxiv on: 17 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 proposed novel gradient projection technique effectively stabilizes sample gradients from previous data by orthogonally projecting back-propagation gradients onto a crucial subspace before using them for weight updates. This technique maintains robustness by collaborating with a class of defense algorithms through sample gradient smoothing, improving the capability of neural networks in terms of robust continual learning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers developed a way to make artificial intelligence systems more secure and reliable. They discovered that when AI learns new things, it forgets how to defend itself against bad data. To fix this, they created a new method called gradient projection that helps AI remember its defenses. This method is important because it keeps AI from getting tricked by fake data, even when the attacks are very strong. |
Keywords
* Artificial intelligence * Continual learning
