Summary of A Learning Paradigm For Interpretable Gradients, by Felipe Torres Figueroa et al.
A Learning Paradigm for Interpretable Gradients
by Felipe Torres Figueroa, Hanwei Zhang, Ronan Sicre, Yannis Avrithis, Stephane Ayache
First submitted to arxiv on: 23 Apr 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: 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 The paper explores ways to improve the interpretability of convolutional neural networks (CNNs) through saliency maps, focusing on Class Activation Maps (CAM). Most approaches combine information from fully connected layers and gradient-based backpropagation. However, gradients are noisy, making alternatives like guided backpropagation necessary for better visualization during inference. The proposed training approach introduces a regularization loss to improve the quality of gradients for interpretability. This results in a less noisy gradient that improves the quantifiable properties of various networks using different interpretability methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is all about making it easier to understand how convolutional neural networks (CNNs) work. Right now, when we look at what parts of an image are most important for a CNN’s decision, those results can be kind of messy and hard to understand. The authors want to fix this by coming up with a new way to train the network so that it produces cleaner and more useful information about which parts of the image matter. |
Keywords
» Artificial intelligence » Backpropagation » Cnn » Inference » Regularization
