2019
Interpretable and Fine-Grained Visual Explanations for Convolutional Neural Networks
CVPR 2019poster
To verify and validate networks, it is essential to gain insight into their decisions, limitations as well as possible shortcomings of training data. In this work, we propose a post-hoc, optimization based visual explanation method, which highlights the evidence in the input image for a specific pre…