SCOUTER: Slot Attention-based Classifier for Explainable Image Recognition

TL;DR

SCOUTER is an attention-based classifier that provides transparent, category-specific explanations directly involved in decision-making, improving interpretability without sacrificing accuracy.

Contribution

It introduces a novel slot attention-based model with explanations tied to confidence scores and positive/negative reasoning, enhancing interpretability in image classification.

Findings

Better visual explanations according to multiple metrics

Maintains high accuracy on small and medium datasets

Offers intuitive, category-specific explanations

Abstract

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.