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Explainable AI (XAI) is an active research area to interpret a neuralnetwork's decision by ensuring transparency and trust in the task-specifiedlearned models. Recently, perturbation-based model analysis has shown betterinterpretation, but backpropagation techniques are still prevailing because oftheir computational efficiency. In this work, we combine both approaches as ahybrid visual explanation algorithm and propose an efficient interpretationmethod for convolutional neural networks. Our method adaptively selects themost critical features that mainly contribute towards a prediction to probe themodel by finding the activated features. Experimental results show that theproposed method can reduce the execution time up to 30% while enhancingcompetitive interpretability without compromising the quality of explanationgenerated.

Ada-SISE: Adaptive Semantic Input Sampling for Efficient Explanation of Convolutional Neural Networks