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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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GrooveSquid.com Paper Summaries

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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