Towards Multi-Scale Visual Explainability for Convolutional Neural Networks
Year: 2020
Type: dissertation
Abstract: Explainability methods seek to find out visual explanations for neural network decisions. Existing techniques mainly fall into two categories: backpropagation- based methods and occlusion-based methods. The former category selectively highlights the computed gradients, while the latter occludes the input to maximally confuse the classifier and visualize the distinct regions. Motivated by the occlusion methods, we propose an explainability model which to our knowledge is the first attempt to extract multi-scale explanations by perturbing the intermediate representations. Furthermore, we present two vi- sualization techniques that can fuse the multi-scale explanations into a single image and suggest an general evaluation metric to assess the explanation’s quality. Both qualitative and quantitative experimental results on several kinds of datasets demonstrate the efficacy of our model.
Author: Song, Yue
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7034582058