# Information-Bottleneck Approach to Salient Region Discovery

## TL;DR

This paper introduces a semi-supervised method for learning Boolean attention masks in images using the Information Bottleneck principle, effectively highlighting class-defining features in synthetic and real datasets.

## Contribution

It presents a novel Boolean mask-based attention model guided by the Information Bottleneck, differing from continuous mask approaches.

## Key findings

- Successfully attends to class-defining features
- Works on synthetic and real datasets
- Produces Boolean masks that conceal irrelevant information

## Abstract

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.