Noise-Free Explanation for Driving Action Prediction

• URL: http://arxiv.org/abs/2407.06339v1
• Date: Mon, 8 Jul 2024 19:21:24 GMT
• Title: Noise-Free Explanation for Driving Action Prediction
• Authors: Hongbo Zhu, Theodor Wulff, Rahul Singh Maharjan, Jinpei Han, Angelo Cangelosi,
• Abstract summary: We propose an easy-to-implement but effective way to remedy this flaw: Smooth Noise Norm Attention (SNNA) We weigh the attention by the norm of the transformed value vector and guide the label-specific signal with the attention gradient, then randomly sample the input perturbations and average the corresponding gradients to produce noise-free attribution. Both qualitative and quantitative evaluation results show the superiority of SNNA compared to other SOTA attention-based explainable methods in generating a clearer visual explanation map and ranking the input pixel importance.
• Score: 11.330363757618379
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Although attention mechanisms have achieved considerable progress in Transformer-based architectures across various Artificial Intelligence (AI) domains, their inner workings remain to be explored. Existing explainable methods have different emphases but are rather one-sided. They primarily analyse the attention mechanisms or gradient-based attribution while neglecting the magnitudes of input feature values or the skip-connection module. Moreover, they inevitably bring spurious noisy pixel attributions unrelated to the model's decision, hindering humans' trust in the spotted visualization result. Hence, we propose an easy-to-implement but effective way to remedy this flaw: Smooth Noise Norm Attention (SNNA). We weigh the attention by the norm of the transformed value vector and guide the label-specific signal with the attention gradient, then randomly sample the input perturbations and average the corresponding gradients to produce noise-free attribution. Instead of evaluating the explanation method on the binary or multi-class classification tasks like in previous works, we explore the more complex multi-label classification scenario in this work, i.e., the driving action prediction task, and trained a model for it specifically. Both qualitative and quantitative evaluation results show the superiority of SNNA compared to other SOTA attention-based explainable methods in generating a clearer visual explanation map and ranking the input pixel importance.

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