Peter Parker or Spiderman? Disambiguating Multiple Class Labels

TL;DR

This paper introduces a novel framework that disambiguates whether multiple top-k predictions from deep networks are driven by distinct entities or a single entity, enhancing interpretability with verifiable counterfactual proofs.

Contribution

It presents a new method combining segmentation and attribution techniques to distinguish between separate and shared entity explanations for multiple class predictions.

Findings

Effective disambiguation on ImageNet samples

Works across multiple models

Provides verifiable counterfactual explanations

Abstract

In the supervised classification setting, during inference, deep networks typically make multiple predictions. For a pair of such predictions (that are in the top-k predictions), two distinct possibilities might occur. On the one hand, each of the two predictions might be primarily driven by two distinct sets of entities in the input. On the other hand, it is possible that there is a single entity or set of entities that is driving the prediction for both the classes in question. This latter case, in effect, corresponds to the network making two separate guesses about the identity of a single entity type. Clearly, both the guesses cannot be true, i.e. both the labels cannot be present in the input. Current techniques in interpretability research do not readily disambiguate these two cases, since they typically consider input attributions for one class label at a time. Here, we present a framework and method to do so, leveraging modern segmentation and input attribution techniques. Notably, our framework also provides a simple counterfactual "proof" of each case, which can be verified for the input on the model (i.e. without running the method again). We demonstrate that the method performs well for a number of samples from the ImageNet validation set and on multiple models.