# Information-Bottleneck Approach to Salient Region Discovery

arXiv: https://arxiv.org/abs/1907.09578 · HTML: https://mlsanity.com/paper/1907.09578/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Jul 2019.

## Built on by (most cited first, 6 of 6)

- [Graph Structure Learning with Variational Information Bottleneck](https://mlsanity.com/paper/2112.08903/) (2021)
- [Reducing Information Bottleneck for Weakly Supervised Semantic Segmentation](https://mlsanity.com/paper/2110.06530/) (2021)
- [R2-Trans:Fine-Grained Visual Categorization with Redundancy Reduction](https://mlsanity.com/paper/2204.10095/) (2022)
- [Information Bottleneck Approach to Spatial Attention Learning](https://mlsanity.com/paper/2108.03418/) (2021)
- [DocVXQA: Context-Aware Visual Explanations for Document Question Answering](https://mlsanity.com/paper/2505.07496/) (2025)
- [Learning Intrinsic Dimension via Information Bottleneck for Explainable Aspect-based Sentiment Analysis](https://mlsanity.com/paper/2402.18145/) (2024)

## Abstract (arXiv)

We propose a new method for learning image attention masks in a semi-supervised setting based on the Information Bottleneck principle. Provided with a set of labeled images, the mask generation model is minimizing mutual information between the input and the masked image while maximizing the mutual information between the same masked image and the image label. In contrast with other approaches, our attention model produces a Boolean rather than a continuous mask, entirely concealing the information in masked-out pixels. Using a set of synthetic datasets based on MNIST and CIFAR10 and the SVHN datasets, we demonstrate that our method can successfully attend to features known to define the image class.