Consistent Explanations by Contrastive Learning

TL;DR

This paper introduces Contrastive Grad-CAM Consistency (CGC), a training method that enhances the consistency of model explanations with human priors while maintaining accuracy, and also improves generalization with unlabeled data.

Contribution

The paper proposes a novel contrastive learning approach to produce more consistent and human-aligned explanations without requiring ground truth annotations.

Findings

CGC produces more consistent Grad-CAM heatmaps aligned with human annotations.

The method maintains comparable classification accuracy.

It acts as a regularizer, improving performance in limited-data and fine-grained tasks.

Abstract

Post-hoc explanation methods, e.g., Grad-CAM, enable humans to inspect the spatial regions responsible for a particular network decision. However, it is shown that such explanations are not always consistent with human priors, such as consistency across image transformations. Given an interpretation algorithm, e.g., Grad-CAM, we introduce a novel training method to train the model to produce more consistent explanations. Since obtaining the ground truth for a desired model interpretation is not a well-defined task, we adopt ideas from contrastive self-supervised learning, and apply them to the interpretations of the model rather than its embeddings. We show that our method, Contrastive Grad-CAM Consistency (CGC), results in Grad-CAM interpretation heatmaps that are more consistent with human annotations while still achieving comparable classification accuracy. Moreover, our method acts as a regularizer and improves the accuracy on limited-data, fine-grained classification settings. In addition, because our method does not rely on annotations, it allows for the incorporation of unlabeled data into training, which enables better generalization of the model. Our code is available here: https://github.com/UCDvision/CGC