Summary of Reinforced Compressive Neural Architecture Search For Versatile Adversarial Robustness, by Dingrong Wang et al.
Reinforced Compressive Neural Architecture Search for Versatile Adversarial Robustness
by Dingrong Wang, Hitesh Sapkota, Zhiqiang Tao, Qi Yu
First submitted to arxiv on: 10 Jun 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 In this research paper, the authors propose a novel approach to neural architecture search (NAS) for achieving versatile adversarial robustness. The current NAS methods have limitations in handling different adversarial attacks and teacher network capacity. To address this challenge, the authors develop Reinforced Compressive Neural Architecture Search (RC-NAS), which combines reinforcement learning with compressive neural networks to effectively expose the agent to diverse attack scenarios and adapt quickly to locate a sub-network for any previously unseen scenarios. The proposed framework achieves adaptive compression towards different initial teacher networks, datasets, and adversarial attacks, resulting in more lightweight and adversarially robust architectures. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research is about finding a way to make neural networks more resistant to attacks that try to make them mistakeful. Currently, the ways we find robust networks are limited and don’t work well for different types of attacks. The authors create a new method called RC-NAS that uses two phases: learning from many scenarios and fine-tuning for specific ones. This helps the network adapt quickly to new situations. The results show that this approach can make more efficient and robust networks. |
Keywords
» Artificial intelligence » Fine tuning » Reinforcement learning
