# Informative Class Activation Maps

Source: [Informative Class Activation Maps](<https://www.sota2.com/research/paper/68fa6a931e209131cea2905f>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T23:28:26.804Z

Authors: Zhenyue Qin, Dongwoo Kim, Tom Gedeon

Published: 2021-06-19T11:02:59.000Z

## Abstract

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.

## Recorded benchmark results

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## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2106.10472>)

[PDF](<https://arxiv.org/pdf/2106.10472.pdf>)