# Peter Parker or Spiderman? Disambiguating Multiple Class Labels

arXiv: https://arxiv.org/abs/2410.19479 · HTML: https://mlsanity.com/paper/2410.19479/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 20 references that are in the map.
- First posted on arXiv 25 Oct 2024.

## Builds on (most cited first, 15 of 15)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Axiomatic Attribution for Deep Networks](https://mlsanity.com/paper/1703.01365/) (2017)
- [Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps](https://mlsanity.com/paper/1312.6034/) (2013)
- [Visualizing and Understanding Convolutional Networks](https://mlsanity.com/paper/1311.2901/) (2013)
- [Learning Important Features Through Propagating Activation Differences](https://mlsanity.com/paper/1704.02685/) (2017)
- [Striving for Simplicity: The All Convolutional Net](https://mlsanity.com/paper/1412.6806/) (2014)
- [Are we done with ImageNet?](https://mlsanity.com/paper/2006.07159/) (2020)
- [XRAI: Better Attributions Through Regions](https://mlsanity.com/paper/1906.02825/) (2019)

## Abstract (arXiv)

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.