Summary of Reliable Evaluation Of Attribution Maps in Cnns: a Perturbation-based Approach, by Lars Nieradzik and Henrike Stephani and Janis Keuper
Reliable Evaluation of Attribution Maps in CNNs: A Perturbation-Based Approach
by Lars Nieradzik, Henrike Stephani, Janis Keuper
First submitted to arxiv on: 22 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 A novel approach is proposed for evaluating attribution maps in convolutional neural networks (CNNs), which are crucial for interpreting predictions. The existing insertion/deletion metrics are shown to be vulnerable to distribution shifts, making their rankings unreliable. To address this issue, the authors suggest replacing pixel modifications with adversarial perturbations, providing a more robust evaluation framework. The effectiveness of this approach is demonstrated using smoothness and monotonicity measures. Additionally, a comprehensive assessment of attribution maps is conducted, introducing baseline maps as sanity checks. The results show that the proposed metric is the only one to pass all checks, achieving increased consistency across 15 dataset-architecture combinations. This research contributes to the development of attribution maps by providing a reliable and consistent evaluation framework. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Attribution maps in convolutional neural networks (CNNs) help us understand how these powerful AI models make predictions. But right now, there’s no good way to test whether these maps are accurate or not. The authors of this paper propose a new approach that makes attribution maps more reliable by replacing old methods with better ones. They also show that their method works well on many different datasets and architectures. This research helps us develop more trustworthy attribution maps for AI models. |
