Explaining the Mistakes of Neural Networks with Latent Sympathetic Examples
Neural networks make mistakes. The reason why a mistake is made often remains a mystery. As such neural networks often are considered a black box. It would be useful to have a method that can give an explanation that is intuitive to a user as to why an image is misclassified. In this paper we develop a method for explaining the mistakes of a classifier model by visually showing what must be added to an image such that it is correctly classified. Our work combines the fields of adversarial examples, generative modeling and a correction technique based on difference target propagation to create an technique that creates explanations of why an image is misclassified. In this paper we explain our method and demonstrate it on MNIST and CelebA. This approach could aid in demystifying neural networks for a user.
 Summary: The authors propose a method for explaining why neural networks make mistakes by learning how to modify an image on a mistaken classification to make it a correct classification. They do this by perturbing the image in an encoded latent space and then reconstructing the perturbed image. The explanation is the difference between the reconstructed perturbed encoded image and the reconstructed original encoded image. The title is too general as this paper only offers an explanation in the area of image classification, which by itself, is still interesting. A method for explaining the results of neural networks is still open ended and visually to the human eye, this paper does offer an explanation of why the 8 is misclassified. However, if this works very well for MNIST, more examples should be given. This single example is interesting but not sufficient to illustrate the success of this method. The examples from CelebA are interesting but inconclusive. For example, why should adding blue to the glasses fix the misclassification. If the explanation is simply visual for a human, then this explanation does not suffice. And the two examples with one receding and the other not receding hairlines look like their correct classifications could be flipped. Regarding epsilon, it is unclear what a small euclidean distance for epsilon is without more examples. It would also help to see how the euclidean distance changes along the path. But also it is not clear why we care about the size of epsilon, but rather the size of the perturbation that must be made to the original image, which is what is defined in the paper as the explanation. Since it is the encoded image that is perturbed, and this is what causes the perturbations to be selective to particular features of the image, an analysis of what features in the encoded space that are modified would greatly help in the interpretability of this explanation. The fact that perturbations are made in the latent space, and that this perturbation gets reflected in particular areas in the reconstructed image, is the most interesting part of this work. More discussion around this would greatly enhance the paper, especially since the technical tools of this method are not very strong. Pros: Interesting explanation, visually selects certain parts of the image relevant to classification rather than obscure pixels Cons: No discussion or analysis about the latent space where perturbations occur. Only one easy example from MNIST shown and examples on CelebA are not great. No way (suggested) to use this tool outside of image recognition.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Explaining the Mistakes of Neural Networks with Latent Sympathetic Examples
Neural networks make mistakes. The reason why a mistake is made often remains a mystery. As such neural networks often are considered a black box. It would be useful to have a method that can give an explanation that is intuitive to a user as to why an image is misclassified. In this paper we develop a method for explaining the mistakes of a classifier model by visually showing what must be added to an image such that it is correctly classified. Our work combines the fields of adversarial examples, generative modeling and a correction technique based on difference target propagation to create an technique that creates explanations of why an image is misclassified. In this paper we explain our method and demonstrate it on MNIST and CelebA. This approach could aid in demystifying neural networks for a user.
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>



 Summary: The authors propose a method for explaining why neural networks make mistakes by learning how to modify an image on a mistaken classification to make it a correct classification. They do this by perturbing the image in an encoded latent space and then reconstructing the perturbed image. The explanation is the difference between the reconstructed perturbed encoded image and the reconstructed original encoded image. The title is too general as this paper only offers an explanation in the area of image classification, which by itself, is still interesting. A method for explaining the results of neural networks is still open ended and visually to the human eye, this paper does offer an explanation of why the 8 is misclassified. However, if this works very well for MNIST, more examples should be given. This single example is interesting but not sufficient to illustrate the success of this method. The examples from CelebA are interesting but inconclusive. For example, why should adding blue to the glasses fix the misclassification. If the explanation is simply visual for a human, then this explanation does not suffice. And the two examples with one receding and the other not receding hairlines look like their correct classifications could be flipped. Regarding epsilon, it is unclear what a small euclidean distance for epsilon is without more examples. It would also help to see how the euclidean distance changes along the path. But also it is not clear why we care about the size of epsilon, but rather the size of the perturbation that must be made to the original image, which is what is defined in the paper as the explanation. Since it is the encoded image that is perturbed, and this is what causes the perturbations to be selective to particular features of the image, an analysis of what features in the encoded space that are modified would greatly help in the interpretability of this explanation. The fact that perturbations are made in the latent space, and that this perturbation gets reflected in particular areas in the reconstructed image, is the most interesting part of this work. More discussion around this would greatly enhance the paper, especially since the technical tools of this method are not very strong. Pros: Interesting explanation, visually selects certain parts of the image relevant to classification rather than obscure pixels Cons: No discussion or analysis about the latent space where perturbations occur. Only one easy example from MNIST shown and examples on CelebA are not great. No way (suggested) to use this tool outside of image recognition.
assistant
 Summary: The authors propose a method for explaining why neural networks make mistakes by learning how to modify an image on a mistaken classification to make it a correct classification. They do this by perturbing the image in an encoded latent space and then reconstructing the perturbed image. The explanation is the difference between the reconstructed perturbed encoded image and the reconstructed original encoded image. The title is too general as this paper only offers an explanation in the area of image classification, which by itself, is still interesting. A method for explaining the results of neural networks is still open ended and visually to the human eye, this paper does offer an explanation of why the 8 is misclassified. However, if this works very well for MNIST, more examples should be given. This single example is interesting but not sufficient to illustrate the success of this method. The examples from CelebA are interesting but inconclusive. For example, why should adding blue to the glasses fix the misclassification. If the explanation is simply visual for a human, then this explanation does not suffice. And the two examples with one receding and the other not receding hairlines look like their correct classifications could be flipped. Regarding epsilon, it is unclear what a small euclidean distance for epsilon is without more examples. It would also help to see how the euclidean distance changes along the path. But also it is not clear why we care about the size of epsilon, but rather the size of the perturbation that must be made to the original image, which is what is defined in the paper as the explanation. Since it is the encoded image that is perturbed, and this is what causes the perturbations to be selective to particular features of the image, an analysis of what features in the encoded space that are modified would greatly help in the interpretability of this explanation. The fact that perturbations are made in the latent space, and that this perturbation gets reflected in particular areas in the reconstructed image, is the most interesting part of this work. More discussion around this would greatly enhance the paper, especially since the technical tools of this method are not very strong. Pros: Interesting explanation, visually selects certain parts of the image relevant to classification rather than obscure pixels Cons: No discussion or analysis about the latent space where perturbations occur. Only one easy example from MNIST shown and examples on CelebA are not great. No way (suggested) to use this tool outside of image recognition.