Consistent Explanations by Contrastive Learning
This addresses the issue of unreliable model interpretations for users in fields like computer vision, offering an incremental improvement by enhancing existing explanation methods without requiring annotations.
The paper tackles the problem of inconsistent explanations from post-hoc methods like Grad-CAM by introducing a training method based on contrastive self-supervised learning to improve consistency with human priors, resulting in more human-aligned heatmaps while maintaining comparable classification accuracy and acting as a regularizer to boost accuracy in limited-data settings.
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