# Informative Class Activation Maps

arXiv: https://arxiv.org/abs/2106.10472 · HTML: https://mlsanity.com/paper/2106.10472/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 14 references that are in the map.
- First posted on arXiv 19 Jun 2021.

## Builds on (most cited first, 8 of 8)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [Learning Deep Features for Discriminative Localization](https://mlsanity.com/paper/1512.04150/) (2015)
- [Network In Network](https://mlsanity.com/paper/1312.4400/) (2013)
- [Conditional Image Synthesis With Auxiliary Classifier GANs](https://mlsanity.com/paper/1610.09585/) (2016)
- [Attention-based Dropout Layer for Weakly Supervised Object Localization](https://mlsanity.com/paper/1908.10028/) (2019)
- [Rethinking Softmax with Cross-Entropy: Neural Network Classifier as Mutual Information Estimator](https://mlsanity.com/paper/1911.10688/) (2019)
- [Improved Techniques For Weakly-Supervised Object Localization](https://mlsanity.com/paper/1802.07888/) (2018)

## Built on by (most cited first, 2 of 2)

- [Attention Guided CAM: Visual Explanations of Vision Transformer Guided by Self-Attention](https://mlsanity.com/paper/2402.04563/) (2024)
- [Context-based Interpretable Spatio-Temporal Graph Convolutional Network for Human Motion Forecasting](https://mlsanity.com/paper/2402.19237/) (2024)

## Abstract (arXiv)

We study how to evaluate the quantitative information content of a region within an image for a particular label. To this end, we bridge class activation maps with information theory. We develop an informative class activation map (infoCAM). Given a classification task, infoCAM depict how to accumulate information of partial regions to that of the entire image toward a label. Thus, we can utilise infoCAM to locate the most informative features for a label. When applied to an image classification task, infoCAM performs better than the traditional classification map in the weakly supervised object localisation task. We achieve state-of-the-art results on Tiny-ImageNet.