PACE: Posthoc Architecture-Agnostic Concept Extractor for Explaining CNNs

• URL: http://arxiv.org/abs/2108.13828v1
• Date: Tue, 31 Aug 2021 13:36:15 GMT
• Title: PACE: Posthoc Architecture-Agnostic Concept Extractor for Explaining CNNs
• Authors: Vidhya Kamakshi, Uday Gupta and Narayanan C Krishnan
• Abstract summary: We introduce a Posthoc Architecture-agnostic Concept Extractor (PACE) that automatically extracts smaller sub-regions of the image. PACE tightly integrates the faithfulness of the explanatory framework to the black-box model. The results from these experiments suggest that over 72% of the concepts extracted by PACE are human interpretable.
• Score: 3.0724051098062097
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

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