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ADA-SISE: ADAPTIVE SEMANTIC INPUT SAMPLING FOR EFFICIENT EXPLANATION OF CONVOLUTIONAL NEURAL NETWORKS

Citation Author(s):
Mahesh Sudhakar, Sam Sattarzadeh, Konstantinos N. Plataniotis, Jongseong Jang, Yeonjeong Jeong, Hyunwoo Kim
Submitted by:
Mahesh Sudhakar
Last updated:
28 June 2021 - 12:27pm
Document Type:
Presentation Slides
Document Year:
2021
Event:
Presenters Name:
Mahesh Sudhakar
Paper Code:
IVMSP-8.5

Abstract 

Abstract: 

Explainable AI (XAI) is an active research area to interpret a neural network’s decision by ensuring transparency and trust in the task-specified learned models.Recently,perturbation-based model analysis has shown better interpretation, but back-propagation techniques are still prevailing because of their computational efficiency. In this work, we combine both approaches as a hybrid visual explanation algorithm and propose an efficient interpretation method for convolutional neural networks. Our method adaptively selects the most critical features that mainly contribute towards a prediction to probe the model by finding the activated features. Experimental results show that the proposed method can reduce the execution time up to 30% while enhancing competitive interpretability without compromising the quality of explanation generated.

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Dataset Files

Presentation slide deck of Ada-SISE XAI algorithm

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Poster of Ada-SISE XAI algorithm

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