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Although deep convolutional neural networks achieve state-of-the-artperformance across nearly all image classification tasks, their decisions aredifficult to interpret. One approach that offers some level of interpretabilityby design is \textit{hard attention}, which uses only relevant portions of theimage. However, training hard attention models with only class labelsupervision is challenging, and hard attention has proved difficult to scale tocomplex datasets. Here, we propose a novel hard attention model, which we termSaccader. Key to Saccader is a pretraining step that requires only class labelsand provides initial attention locations for policy gradient optimization. Ourbest models narrow the gap to common ImageNet baselines, achieving $75\%$ top-1and $91\%$ top-5 while attending to less than one-third of the image.

Saccader: Improving Accuracy of Hard Attention Models for Vision