Anomalous behaviour in loss-gradient based interpretability methods

TL;DR

This paper investigates unexpected phenomena in loss-gradient based interpretability methods, revealing that occluding parts of input can sometimes improve model performance, challenging assumptions about interpretability techniques.

Contribution

The study systematically evaluates loss-gradient attribution methods and uncovers conditions under which occlusion enhances model performance, providing new insights into interpretability.

Findings

Occlusion can improve model performance in certain conditions.

Loss-gradient methods exhibit anomalous behavior across tasks.

Different occlusion levels and replacements influence interpretability results.

Abstract

Loss-gradients are used to interpret the decision making process of deep learning models. In this work, we evaluate loss-gradient based attribution methods by occluding parts of the input and comparing the performance of the occluded input to the original input. We observe that the occluded input has better performance than the original across the test dataset under certain conditions. Similar behaviour is observed in sound and image recognition tasks. We explore different loss-gradient attribution methods, occlusion levels and replacement values to explain the phenomenon of performance improvement under occlusion.