PACE: Posthoc Architecture-Agnostic Concept Extractor for Explaining CNNs
Abstract
Deep CNNs, though have achieved the state of the art performance in image classification tasks, remain a black-box to a human using them. There is a growing interest in explaining the working of these deep models to improve their trustworthiness. In this paper, we introduce a Posthoc Architecture-agnostic Concept Extractor (PACE) that automatically extracts smaller sub-regions of the image called concepts relevant to the black-box prediction. PACE tightly integrates the faithfulness of the explanatory framework to the black-box model. To the best of our knowledge, this is the first work that extracts class-specific discriminative concepts in a posthoc manner automatically. The PACE framework is used to generate explanations for two different CNN architectures trained for classifying the AWA2 and Imagenet-Birds datasets. Extensive human subject experiments are conducted to validate the human interpretability and consistency of the explanations extracted by PACE. The results from these experiments suggest that over 72% of the concepts extracted by PACE are human interpretable.
Cite
@article{arxiv.2108.13828,
title = {PACE: Posthoc Architecture-Agnostic Concept Extractor for Explaining CNNs},
author = {Vidhya Kamakshi and Uday Gupta and Narayanan C Krishnan},
journal= {arXiv preprint arXiv:2108.13828},
year = {2021}
}
Comments
Accepted at International Joint Conference on Neural Networks (IJCNN 2021)