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Pace: Posthoc architecture-agnostic concept extractor for explaining cnns

Publication Type : Journal Article

Thematic Areas : Center for Computational Engineering and Networking (CEN)

Source : International Joint Conference on Neural Networks (IJCNN), 2021

Url :

Campus : Coimbatore

Year : 2021

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 this Research Publication : Vidhya Kamakshi, Uday Gupta, Narayanan C Krishnan "PACE: Posthoc Architecture-Agnostic Concept Extractor for Explaining CNNs", International Joint Conference on Neural Networks (IJCNN), 2021

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