SCOUTER: Slot Attention-based Classifier for Explainable Image Recognition
This addresses the need for more interpretable AI in image classification, offering a novel approach to generate explanations directly involved in decision-making, though it is incremental in improving existing attention-based methods.
The paper tackles the problem of explainable image recognition by proposing SCOUTER, a slot attention-based classifier that provides transparent and accurate classification with intuitive positive or negative explanations for each category, achieving good accuracy on small and medium-sized datasets.
Explainable artificial intelligence has been gaining attention in the past few years. However, most existing methods are based on gradients or intermediate features, which are not directly involved in the decision-making process of the classifier. In this paper, we propose a slot attention-based classifier called SCOUTER for transparent yet accurate classification. Two major differences from other attention-based methods include: (a) SCOUTER's explanation is involved in the final confidence for each category, offering more intuitive interpretation, and (b) all the categories have their corresponding positive or negative explanation, which tells "why the image is of a certain category" or "why the image is not of a certain category." We design a new loss tailored for SCOUTER that controls the model's behavior to switch between positive and negative explanations, as well as the size of explanatory regions. Experimental results show that SCOUTER can give better visual explanations in terms of various metrics while keeping good accuracy on small and medium-sized datasets.