Reviews: Saccader: Improving Accuracy of Hard Attention Models for Vision

This paper addresses the problem of training hard-attention mechanisms on image classification. To do so, it introduces a new hard-attention layer (called a Saccader cell) with a pretraining procedure that improves performance. More importantly, they show that the approch is more interpretable requiring fewer glimpses than other methods while outperforming other similar approches and being close in performance to non-intepretable models such as ResNet. Originality: The proposed Saccader model is original and compares favorably to state of the art works in term of performance and also, more importantly, interpretability. Related work has been cited adequately.