Learning Support and Trivial Prototypes for Interpretable Image Classification

Author:

Wang Chong1,Liu Yuyuan1,Chen Yuanhong1,Liu Fengbei1,Tian Yu2,McCarthy Davis3,Frazer Helen4,Carneiro Gustavo5

Affiliation:

1. University of Adelaide,Australian Institute for Machine Learning

2. Harvard University

3. St Vincent’s Institute of Medical Research

4. St Vincent’s Hospital Melbourne

5. University of Surrey,CVSSP

Funder

Australian Government

Australian Research Council

Publisher

IEEE

Reference60 articles.

1. Towards robust interpretability with self-explaining neural networks;Melis;Advances in Neural Information Processing Systems,2018

2. A case-based interpretable deep learning model for classification of mass lesions in digital mammography

3. B-cos Networks: Alignment is All We Need for Interpretability

4. This looks like that: deep learning for interpretable image recognition;Chen;Advances in Neural Information Processing Systems,2019

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