Explaining the prediction of deep models has gained increasing attention to increase its applicability, even spreading it to life-affecting decisions. However there has been no attempt to pinpoint only the most discriminative features contributing specifically to separating different classes in a fine-grained classification task. This paper introduces a novel notion of salient explanation and proposes a simple yet effective salient explanation method called Gaussian light and shadow (GLAS), which estimates the spatial impact of deep models by the feature perturbation inspired by light and shadow in nature. GLAS provides a useful coarseto-fine control benefiting from scalability of Gaussian mask. We also devised the ability to identify multiple instances through recursive GLAS. We prove the effectiveness of GLAS for fine-grained classification using the fine-grained classification dataset. To show the general applicability, we also illustrate that GLAS has state-of-the-art performance at high speed (about 0.5 sec per 224×224 image) via the ImageNet Large Scale Visual Recognition Challenge.
The paper introduces a new method in the family of local perturbation-based interpretations for deep networks and more specifically for fine-grained classification tasks. Compared to other saliency map methods (gradient-based, propagation-based, etc), this family has the advantage of needing only black-box access to the model. The introduced method GLAS, scans over an image and lights/shadows each part of the image to assign an importance score to different regions of the image based on the change in model's prediction. The motivation of this work is to increase the inherently low speed of (some) methods in this family and to give better explanations by 
I vote for rejecting this paper as the contributions to what already exists in the literature are not clear and the provided experimental results are not convincing.
Compared to previous perturbation-based methods, the main advantage seems to be speed. There are perturbation-based methods that do not suffer from low speed e.g. Dabkowski & Gal and give real-time perturbation-based saliency maps. The authors do not mention this work (and similar works) and do not compare both their speed and their performance against it.
One important problem (probably the most important) with the perturbation-based saliency maps is the fact that the perturbations might push a given image out of the true data manifold and therefore give an invalid interpretation of the model. The authors do not discuss the matter and how their method would address this issue. Intuitively, the introduced algorithm, more specifically the RGLAS algorithm, seems to suffer from this issue not any less than other existing methods.
The experimental results seek to demonstrate the superiority of the introduced method over rival methods. The justification behind the provided visual examples is their focus on more discriminative features (in human eyes). This does not necessarily mean that a given saliency map is more "truceful"; i.e. having a more visually appealing saliency map has nothing to do with a more truthful explanation of a model's decision making. The results focused on the mistakes of the model seem more convincing and interesting.
 The objective results first focus on the target localization metric which has traditionally been used in the literature. Although it is much faster to execute, the introduced method is only marginally superior to other methods. The most important problem, however, is that as mentioned above, there are fast methods in the literature and therefore a fair objective comparison is should include other methods as well. Secondly, the IOU measure is used. GLAS is not compared to other methods in this metric.
The authors mention the effectiveness of their work for "fine-grained" classification tasks while throughout the paper there is no convincing evidence or discussion that the method is curated for such tasks. As mentioned above, changing the scale parameter for getting more visuall appealing saliency maps is not enough evidence.
All in all, the true contribution of this work to other existing methods in this family is not enough for this venue.
A few questions and suggestions:
* How should one adjust the scale parameter? In other words, what is the hyper-parameter search scheme for this method which would make it robust against the human-biased choice of hyper-parameters which would result in visually more appealing saliency maps but not necessarily explain the model?
* Explanation of RGLAS is not clear.
* For a general reader, metrics such as IOU should be explained more clearly.
* The paper has many many typing errors.