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 …