Robust saliency maps with distribution-preserving decoys
Saliency methods help to make deep neural network predictions more interpretable by identifying particular features, such as pixels in an image, that contribute most strongly to the network*s prediction. Unfortunately, recent evidence suggests that many saliency methods perform poorly when gradients are saturated or in the presence of strong inter-feature dependence or noise injected by an adversarial attack. In this work, we propose a data-driven technique that uses the distribution-preserving decoys to infer robust saliency scores in conjunction with a pre-trained convolutional neural network classifier and any off-the-shelf saliency method. We formulate the generation of decoys as an optimization problem, potentially applicable to any convolutional network architecture. We also propose a novel decoy-enhanced saliency score, which provably compensates for gradient saturation and considers joint activation patterns of pixels in a single-layer convolutional neural network. Empirical results on the ImageNet data set using three different deep neural network architectures---VGGNet, AlexNet and ResNet---show both qualitatively and quantitatively that decoy-enhanced saliency scores outperform raw scores produced by three existing saliency methods.
I Summary This paper has two major contributions: - A method to infer robust saliency maps using distribution preserving decoys (generated perturbated images that resemble the intermediate representation of the original image in the neural network) - A decoy-enhanced saliency score which compensates for gradient saturation and takes into account joint activation patterns The authors show the performance of their methods on three different saliency methods, quantitatively and qualitatively, but also when it is submitted to adversarial perturbations. II Comments 1. Content The paper is very clear and easy to read, I would like to point out how well structured it is. The method yields very interesting results, the whole work is very complete experimentally (saliency methods used, adversarial perturbations, models etc) and the process coherent. I would love to see it applied to different datasets: Imagenet focuses more on the foreground, whereas *Places* focuses more on background objects and could be interesting to use. The authors referred to Sanity Checks for Saliency Maps (Adebayo et al) without using it for their results, it would be nice to add it to the experiments. 2. Typos - intro, paragraph 2, l2: use -> uses - paragraph 5, l9: indendently -> independently - 4.2, paragraph 4, last line: target word twice - 4.3, paragraph 2, l5: fisrt -> first - l9: not able -> may not be able - 4.5, l5: salinency -> saliency
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Robust saliency maps with distribution-preserving decoys
Saliency methods help to make deep neural network predictions more interpretable by identifying particular features, such as pixels in an image, that contribute most strongly to the network*s prediction. Unfortunately, recent evidence suggests that many saliency methods perform poorly when gradients are saturated or in the presence of strong inter-feature dependence or noise injected by an adversarial attack. In this work, we propose a data-driven technique that uses the distribution-preserving decoys to infer robust saliency scores in conjunction with a pre-trained convolutional neural network classifier and any off-the-shelf saliency method. We formulate the generation of decoys as an optimization problem, potentially applicable to any convolutional network architecture. We also propose a novel decoy-enhanced saliency score, which provably compensates for gradient saturation and considers joint activation patterns of pixels in a single-layer convolutional neural network. Empirical results on the ImageNet data set using three different deep neural network architectures---VGGNet, AlexNet and ResNet---show both qualitatively and quantitatively that decoy-enhanced saliency scores outperform raw scores produced by three existing saliency methods.
user
<think>Okay, to do a decent peer review, I should start with summarizing what the work is about. This includes novelty, contributions, clarity, and impact. I might include suggestions to issues that I find critical to the usefulness of the work. Starting with novelty, there are different types of novelty. The first aspect of novelty is scope and scale. Is the work covering a broader perspective than previous works? The second aspect is methodology. Is the proposed methodology novel compared to previous methods? Do they provide any significant advantage over previous methodologies? The third aspect is results. Are the results different compared previous work? Does this lead to any novel theories or an extension of current theories? Next is clarity, I will be reading the work in depth and analyze if any sentences can be potentially ambiguous or misleading. If any sentences are ambiguous, I will state them and give a suggestion on how to rephrase such sentence or how to make more clear to the audience. Lastly, impact is important. I will attempt to predict how such work will impact the scientific community. This might include the applications of such work and how the work might transform the way we study a certain topic. Finally, I will might only mention issues that are critical. This is to keep the review as concise and helpful as possible.</think>



I Summary This paper has two major contributions: - A method to infer robust saliency maps using distribution preserving decoys (generated perturbated images that resemble the intermediate representation of the original image in the neural network) - A decoy-enhanced saliency score which compensates for gradient saturation and takes into account joint activation patterns The authors show the performance of their methods on three different saliency methods, quantitatively and qualitatively, but also when it is submitted to adversarial perturbations. II Comments 1. Content The paper is very clear and easy to read, I would like to point out how well structured it is. The method yields very interesting results, the whole work is very complete experimentally (saliency methods used, adversarial perturbations, models etc) and the process coherent. I would love to see it applied to different datasets: Imagenet focuses more on the foreground, whereas *Places* focuses more on background objects and could be interesting to use. The authors referred to Sanity Checks for Saliency Maps (Adebayo et al) without using it for their results, it would be nice to add it to the experiments. 2. Typos - intro, paragraph 2, l2: use -> uses - paragraph 5, l9: indendently -> independently - 4.2, paragraph 4, last line: target word twice - 4.3, paragraph 2, l5: fisrt -> first - l9: not able -> may not be able - 4.5, l5: salinency -> saliency
assistant
I Summary This paper has two major contributions: - A method to infer robust saliency maps using distribution preserving decoys (generated perturbated images that resemble the intermediate representation of the original image in the neural network) - A decoy-enhanced saliency score which compensates for gradient saturation and takes into account joint activation patterns The authors show the performance of their methods on three different saliency methods, quantitatively and qualitatively, but also when it is submitted to adversarial perturbations. II Comments 1. Content The paper is very clear and easy to read, I would like to point out how well structured it is. The method yields very interesting results, the whole work is very complete experimentally (saliency methods used, adversarial perturbations, models etc) and the process coherent. I would love to see it applied to different datasets: Imagenet focuses more on the foreground, whereas *Places* focuses more on background objects and could be interesting to use. The authors referred to Sanity Checks for Saliency Maps (Adebayo et al) without using it for their results, it would be nice to add it to the experiments. 2. Typos - intro, paragraph 2, l2: use -> uses - paragraph 5, l9: indendently -> independently - 4.2, paragraph 4, last line: target word twice - 4.3, paragraph 2, l5: fisrt -> first - l9: not able -> may not be able - 4.5, l5: salinency -> saliency