NeurIPS 2019
Sun Dec 8th through Sat the 14th, 2019 at Vancouver Convention Center
Paper ID:6347
Title:Attribution-Based Confidence Metric For Deep Neural Networks

This paper is well-motivated, written clearly, and provides theoretical and empirical evidence for the utility of integrated gradients for computing confidence scores of neural networks. The ideas presented are novel and are backed up quite well with theory and experiments. Few suggestions for improvement in the final version of the paper: 1. simple demo against Platt scaling 2. clarification of the sparseness of IG attribution maps 3. a more detailed qualitative error analysis of confidence metric All in all, this is a good contribution and I recommend its acceptance.