SPIGAN: Privileged Adversarial Learning from Simulation
Deep Learning for Computer Vision depends mainly on the source of supervision. Photo-realistic simulators can generate large-scale automatically labeled synthetic data, but introduce a domain gap negatively impacting performance. We propose a new unsupervised domain adaptation algorithm, called SPIGAN, relying on Simulator Privileged Information (PI) and Generative Adversarial Networks (GAN). We use internal data from the simulator as PI during the training of a target task network. We experimentally evaluate our approach on semantic segmentation. We train the networks on real-world Cityscapes and Vistas datasets, using only unlabeled real-world images and synthetic labeled data with z-buffer (depth) PI from the SYNTHIA dataset. Our method improves over no adaptation and state-of-the-art unsupervised domain adaptation techniques.
 The paper focuses on the problem of semantic segmentation across domains. The most standard setting for this task involves real world street images as target and synthetic domains as sources with images produced by simulators of photo-realistic hurban scenes. This work proposes to leverage further depth information which is actually produced by the simulator together with the source images but which is in general not taken into consideration. The used deep architecture is a GAN where the generator learning is guided by three components: (1) the standard discriminator loss (2) the cross entropy loss for image segmentation that evaluates the correct label assignment to each image pixel (3) an l1-based loss which evaluates the correct prediction of the depth values in the original and generated image. A further perceptual regularizer is introduced to support the learning. + overall the paper is well organized and easy to read + the proposed idea is smart: when starting from a synthetic domain there may be several hidden extra information that are generally neglected but that can instead support the learning task + the experimental results seem promising Still, I have some concerns - if the main advantage of the proposed approach is in the introduction of the priviledged information, I would expect that disactivating the related PI loss we should get back to results analogous of those obtained by other competing methods. However from Table 2 it seems that SPIGAN-no-PI is already much better than the FCN Source baseline in the Cityscape case and much worse in the Vistas case. This should be better clarified -- are the basic structure of SPIGAN and FCN analogous? - the ablation does not cover an analysis on the role of the perceptual regularizer. This is also related to the point above: the use of a perceptual loss may introduce a basic difference with respect to competing methods. It should be better discussed. - section 4.1 mentions the use of a validation set. More details should be provided about it and on how the hyperparameters were chosen. A possible analysis on the robustness of the method to those parameters could provide some further intuition about the network stability. It might be also interesting to check if the the loss weights provide some intuition about the relative importance of the losses in the learning process. - the negative transfer rate is another way to measure the advantage of using the PI with respect to not using it. However, since it is not evaluated for the competing methods its value does not add much information and indeed it is only quickly mentioned in the text. It should be better discussed. - some recent papers have shown better results than those considered here as baseline: [Learning to Adapt Structured Output Space for Semantic Segmentation, CVPR 2018] [Unsupervised Domain Adaptation for Semantic Segmentation via Class-Balanced Self-Training, ECCV 2018] they should be included as related work and considered as reference for the experimental results. Overall I think that the proposed idea is valuable but the paper should better clarify the points mentioned above.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
SPIGAN: Privileged Adversarial Learning from Simulation
Deep Learning for Computer Vision depends mainly on the source of supervision. Photo-realistic simulators can generate large-scale automatically labeled synthetic data, but introduce a domain gap negatively impacting performance. We propose a new unsupervised domain adaptation algorithm, called SPIGAN, relying on Simulator Privileged Information (PI) and Generative Adversarial Networks (GAN). We use internal data from the simulator as PI during the training of a target task network. We experimentally evaluate our approach on semantic segmentation. We train the networks on real-world Cityscapes and Vistas datasets, using only unlabeled real-world images and synthetic labeled data with z-buffer (depth) PI from the SYNTHIA dataset. Our method improves over no adaptation and state-of-the-art unsupervised domain adaptation techniques.
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 paper focuses on the problem of semantic segmentation across domains. The most standard setting for this task involves real world street images as target and synthetic domains as sources with images produced by simulators of photo-realistic hurban scenes. This work proposes to leverage further depth information which is actually produced by the simulator together with the source images but which is in general not taken into consideration. The used deep architecture is a GAN where the generator learning is guided by three components: (1) the standard discriminator loss (2) the cross entropy loss for image segmentation that evaluates the correct label assignment to each image pixel (3) an l1-based loss which evaluates the correct prediction of the depth values in the original and generated image. A further perceptual regularizer is introduced to support the learning. + overall the paper is well organized and easy to read + the proposed idea is smart: when starting from a synthetic domain there may be several hidden extra information that are generally neglected but that can instead support the learning task + the experimental results seem promising Still, I have some concerns - if the main advantage of the proposed approach is in the introduction of the priviledged information, I would expect that disactivating the related PI loss we should get back to results analogous of those obtained by other competing methods. However from Table 2 it seems that SPIGAN-no-PI is already much better than the FCN Source baseline in the Cityscape case and much worse in the Vistas case. This should be better clarified -- are the basic structure of SPIGAN and FCN analogous? - the ablation does not cover an analysis on the role of the perceptual regularizer. This is also related to the point above: the use of a perceptual loss may introduce a basic difference with respect to competing methods. It should be better discussed. - section 4.1 mentions the use of a validation set. More details should be provided about it and on how the hyperparameters were chosen. A possible analysis on the robustness of the method to those parameters could provide some further intuition about the network stability. It might be also interesting to check if the the loss weights provide some intuition about the relative importance of the losses in the learning process. - the negative transfer rate is another way to measure the advantage of using the PI with respect to not using it. However, since it is not evaluated for the competing methods its value does not add much information and indeed it is only quickly mentioned in the text. It should be better discussed. - some recent papers have shown better results than those considered here as baseline: [Learning to Adapt Structured Output Space for Semantic Segmentation, CVPR 2018] [Unsupervised Domain Adaptation for Semantic Segmentation via Class-Balanced Self-Training, ECCV 2018] they should be included as related work and considered as reference for the experimental results. Overall I think that the proposed idea is valuable but the paper should better clarify the points mentioned above.
assistant
 The paper focuses on the problem of semantic segmentation across domains. The most standard setting for this task involves real world street images as target and synthetic domains as sources with images produced by simulators of photo-realistic hurban scenes. This work proposes to leverage further depth information which is actually produced by the simulator together with the source images but which is in general not taken into consideration. The used deep architecture is a GAN where the generator learning is guided by three components: (1) the standard discriminator loss (2) the cross entropy loss for image segmentation that evaluates the correct label assignment to each image pixel (3) an l1-based loss which evaluates the correct prediction of the depth values in the original and generated image. A further perceptual regularizer is introduced to support the learning. + overall the paper is well organized and easy to read + the proposed idea is smart: when starting from a synthetic domain there may be several hidden extra information that are generally neglected but that can instead support the learning task + the experimental results seem promising Still, I have some concerns - if the main advantage of the proposed approach is in the introduction of the priviledged information, I would expect that disactivating the related PI loss we should get back to results analogous of those obtained by other competing methods. However from Table 2 it seems that SPIGAN-no-PI is already much better than the FCN Source baseline in the Cityscape case and much worse in the Vistas case. This should be better clarified -- are the basic structure of SPIGAN and FCN analogous? - the ablation does not cover an analysis on the role of the perceptual regularizer. This is also related to the point above: the use of a perceptual loss may introduce a basic difference with respect to competing methods. It should be better discussed. - section 4.1 mentions the use of a validation set. More details should be provided about it and on how the hyperparameters were chosen. A possible analysis on the robustness of the method to those parameters could provide some further intuition about the network stability. It might be also interesting to check if the the loss weights provide some intuition about the relative importance of the losses in the learning process. - the negative transfer rate is another way to measure the advantage of using the PI with respect to not using it. However, since it is not evaluated for the competing methods its value does not add much information and indeed it is only quickly mentioned in the text. It should be better discussed. - some recent papers have shown better results than those considered here as baseline: [Learning to Adapt Structured Output Space for Semantic Segmentation, CVPR 2018] [Unsupervised Domain Adaptation for Semantic Segmentation via Class-Balanced Self-Training, ECCV 2018] they should be included as related work and considered as reference for the experimental results. Overall I think that the proposed idea is valuable but the paper should better clarify the points mentioned above.