Thinking like a machine — generating visual rationales through latent space optimization
Interpretability and small labelled datasets are key issues in the practical application of deep learning, particularly in areas such as medicine. In this paper, we present a semi-supervised technique that addresses both these issues simultaneously. We learn dense representations from large unlabelled image datasets, then use those representations to both learn classifiers from small labeled sets and generate visual rationales explaining the predictions. Using chest radiography diagnosis as a motivating application, we show our method has good generalization ability by learning to represent our chest radiography dataset while training a classifier on an separate set from a different institution. Our method identifies heart failure and other thoracic diseases. For each prediction, we generate visual rationales for positive classifications by optimizing a latent representation to minimize the probability of disease while constrained by a similarity measure in image space. Decoding the resultant latent representation produces an image without apparent disease. The difference between the original and the altered image forms an interpretable visual rationale for the algorithm*s prediction. Our method simultaneously produces visual rationales that compare favourably to previous techniques and a classifier that outperforms the current state-of-the-art.
 The main contribution of the paper is a method that provides visual explanations of classification decisions. The proposed method uses - a generator trained in a GAN setup - an autoencoder to obtain a latent space representation - a method inspired by adversarial sample generation to obtain a generated image from another class - which can then be compared to the original image (or rather the reconstruction of it). The method is evaluated on a medical images dataset and some additional demonstration on MNIST is provided. - The paper proposes a (I believe) novel method to obtain visual explanations. The results are visually compelling although most results are shown on a medical dataset - which I feel is very hard for most readers to follow. The MNIST explanations help a lot. It would be great if the authors could come up with an additional way to demonstrate their method to the non-medical reader. - The paper shows that the results are plausible using a neat trick. The authors train their system with the testdata included which leads to very different visualizations. It would be great if this analysis could be performed for MNIST as well. From the related work, it would be nice to mention that generative models (p(x|c)) also often allow for explaining their decisions, e.g. the work by Lake and Tenenbaum on probabilistic program induction. Also, there is the work by Hendricks et al on Generating Visual Explanations. This should probably also be referenced. minor comments: - some figures with just two parts are labeled "from left to right" - it would be better to just write left: ... right: ... - figure 2: do these images correspond to each other? If yes, it would be good to show them pairwise. - figure 5: please explain why the saliency map is relevant. This looks very noisy and non-interesting.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Thinking like a machine — generating visual rationales through latent space optimization
Interpretability and small labelled datasets are key issues in the practical application of deep learning, particularly in areas such as medicine. In this paper, we present a semi-supervised technique that addresses both these issues simultaneously. We learn dense representations from large unlabelled image datasets, then use those representations to both learn classifiers from small labeled sets and generate visual rationales explaining the predictions. Using chest radiography diagnosis as a motivating application, we show our method has good generalization ability by learning to represent our chest radiography dataset while training a classifier on an separate set from a different institution. Our method identifies heart failure and other thoracic diseases. For each prediction, we generate visual rationales for positive classifications by optimizing a latent representation to minimize the probability of disease while constrained by a similarity measure in image space. Decoding the resultant latent representation produces an image without apparent disease. The difference between the original and the altered image forms an interpretable visual rationale for the algorithm*s prediction. Our method simultaneously produces visual rationales that compare favourably to previous techniques and a classifier that outperforms the current state-of-the-art.
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>



 The main contribution of the paper is a method that provides visual explanations of classification decisions. The proposed method uses - a generator trained in a GAN setup - an autoencoder to obtain a latent space representation - a method inspired by adversarial sample generation to obtain a generated image from another class - which can then be compared to the original image (or rather the reconstruction of it). The method is evaluated on a medical images dataset and some additional demonstration on MNIST is provided. - The paper proposes a (I believe) novel method to obtain visual explanations. The results are visually compelling although most results are shown on a medical dataset - which I feel is very hard for most readers to follow. The MNIST explanations help a lot. It would be great if the authors could come up with an additional way to demonstrate their method to the non-medical reader. - The paper shows that the results are plausible using a neat trick. The authors train their system with the testdata included which leads to very different visualizations. It would be great if this analysis could be performed for MNIST as well. From the related work, it would be nice to mention that generative models (p(x|c)) also often allow for explaining their decisions, e.g. the work by Lake and Tenenbaum on probabilistic program induction. Also, there is the work by Hendricks et al on Generating Visual Explanations. This should probably also be referenced. minor comments: - some figures with just two parts are labeled "from left to right" - it would be better to just write left: ... right: ... - figure 2: do these images correspond to each other? If yes, it would be good to show them pairwise. - figure 5: please explain why the saliency map is relevant. This looks very noisy and non-interesting.
assistant
 The main contribution of the paper is a method that provides visual explanations of classification decisions. The proposed method uses - a generator trained in a GAN setup - an autoencoder to obtain a latent space representation - a method inspired by adversarial sample generation to obtain a generated image from another class - which can then be compared to the original image (or rather the reconstruction of it). The method is evaluated on a medical images dataset and some additional demonstration on MNIST is provided. - The paper proposes a (I believe) novel method to obtain visual explanations. The results are visually compelling although most results are shown on a medical dataset - which I feel is very hard for most readers to follow. The MNIST explanations help a lot. It would be great if the authors could come up with an additional way to demonstrate their method to the non-medical reader. - The paper shows that the results are plausible using a neat trick. The authors train their system with the testdata included which leads to very different visualizations. It would be great if this analysis could be performed for MNIST as well. From the related work, it would be nice to mention that generative models (p(x|c)) also often allow for explaining their decisions, e.g. the work by Lake and Tenenbaum on probabilistic program induction. Also, there is the work by Hendricks et al on Generating Visual Explanations. This should probably also be referenced. minor comments: - some figures with just two parts are labeled "from left to right" - it would be better to just write left: ... right: ... - figure 2: do these images correspond to each other? If yes, it would be good to show them pairwise. - figure 5: please explain why the saliency map is relevant. This looks very noisy and non-interesting.