Evaluating the Faithfulness of Saliency-based Explanations for Deep Learning Models for Temporal Colour Constancy
This work addresses the need for trustworthy explanations in video processing, though it is incremental by extending existing NLP methods to a new domain.
The paper tackled the problem of evaluating whether saliency-based explanations like attention accurately reflect the decision-making process of deep learning models, specifically in temporal colour constancy, and found that attention fails to be faithful while confidence-based saliency succeeds.
The opacity of deep learning models constrains their debugging and improvement. Augmenting deep models with saliency-based strategies, such as attention, has been claimed to help get a better understanding of the decision-making process of black-box models. However, some recent works challenged saliency's faithfulness in the field of Natural Language Processing (NLP), questioning attention weights' adherence to the true decision-making process of the model. We add to this discussion by evaluating the faithfulness of in-model saliency applied to a video processing task for the first time, namely, temporal colour constancy. We perform the evaluation by adapting to our target task two tests for faithfulness from recent NLP literature, whose methodology we refine as part of our contributions. We show that attention fails to achieve faithfulness, while confidence, a particular type of in-model visual saliency, succeeds.