Summary of Is Relu Adversarially Robust?, by Korn Sooksatra et al.
Is ReLU Adversarially Robust?
by Korn Sooksatra, Greg Hamerly, Pablo Rivas
First submitted to arxiv on: 6 May 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 paper investigates the role of rectified linear unit (ReLU) activation functions in generating adversarial examples for deep learning models. The vulnerability of these models to adversarial attacks has raised concerns about their reliability and trustworthiness. By analyzing the impact of ReLU functions on the robustness of deep learning models, this study aims to develop a more resilient model that can withstand such attacks. The authors propose a modified version of the ReLU function, which demonstrates improved robustness against adversarial examples through empirical analysis. Furthermore, they experimentally validate the effectiveness of their proposed modification and show that it can be further enhanced by applying adversarial training. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers looked at how deep learning models work with something called ReLU activation functions. These functions help train the models, but this study found that they make the models vulnerable to attacks. The scientists wanted to see if changing these functions could make the models more secure. They tested a new version of the ReLU function and found it worked better against attacks. They also showed that making small changes to the model during training can help it get even stronger. |
Keywords
» Artificial intelligence » Deep learning » Relu
