Summary of Reliable or Deceptive? Investigating Gated Features For Smooth Visual Explanations in Cnns, by Soham Mitra et al.
Reliable or Deceptive? Investigating Gated Features for Smooth Visual Explanations in CNNs
by Soham Mitra, Atri Sukul, Swalpa Kumar Roy, Pravendra Singh, Vinay Verma
First submitted to arxiv on: 30 Apr 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- 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 This research proposes a novel approach, ScoreCAM++, for explainable AI (XAI) in deep learning models. The method builds upon the promising ScoreCAM technique by introducing modifications to enhance visual interpretability. Specifically, the authors modify the normalization function within the activation layer and apply an activation function to upsampled layers, selectively gating lower-priority values. This leads to significantly improved results compared to previous methods. Through extensive experiments and qualitative comparisons, ScoreCAM++ demonstrates superior performance and fairness in interpreting decision-making processes. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study aims to make deep learning models more understandable by developing a new way to explain their decisions. The approach is called ScoreCAM++ and it improves upon an existing method called ScoreCAM. The researchers changed the way some calculations are done in ScoreCAM, which leads to better results. They also added a special function to help understand how the model works. This new method performs well and is fair when compared to other methods. |
Keywords
» Artificial intelligence » Deep learning
