Summary of Attribution For Enhanced Explanation with Transferable Adversarial Exploration, by Zhiyu Zhu et al.
Attribution for Enhanced Explanation with Transferable Adversarial eXploration
by Zhiyu Zhu, Jiayu Zhang, Zhibo Jin, Huaming Chen, Jianlong Zhou, Fang Chen
First submitted to arxiv on: 27 Dec 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
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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 introduces AttEXplore++, a framework that enhances the interpretability of deep neural networks by incorporating transferable adversarial attack methods. It builds upon AttEXplore and significantly improves the accuracy and robustness of model explanations. The authors conduct extensive experiments on five models using the ImageNet dataset, achieving an average performance improvement of 7.57% over AttEXplore and 32.62% compared to other state-of-the-art interpretability algorithms. The method uses insertion and deletion scores as evaluation metrics and demonstrates that adversarial transferability plays a vital role in enhancing attribution results. Additionally, the authors explore the impact of various parameters on attribution performance, showing that AttEXplore++ provides more stable and reliable explanations across models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about making deep neural networks more understandable by using special methods to help us understand how they make decisions. It’s important for things like computer vision. The new method, called AttEXplore++, is better than the old one at explaining how the model works. They tested it on five different models and a big dataset called ImageNet. The results are really good – it’s 7.57% better than before! They also found that some special tricks they used made it even better. The paper shows that this new method is more reliable and gives us better explanations. |
Keywords
» Artificial intelligence » Transferability
