Improving the Generalization of Adversarial Training with Domain Adaptation
By injecting adversarial examples into training data, adversarial training is promising for improving the robustness of deep learning models. However, most existing adversarial training approaches are based on a specific type of adversarial attack. It may not provide sufficiently representative samples from the adversarial domain, leading to a weak generalization ability on adversarial examples from other attacks. Moreover, during the adversarial training, adversarial perturbations on inputs are usually crafted by fast single-step adversaries so as to scale to large datasets. This work is mainly focused on the adversarial training yet efficient FGSM adversary. In this scenario, it is difficult to train a model with great generalization due to the lack of representative adversarial samples, aka the samples are unable to accurately reflect the adversarial domain. To alleviate this problem, we propose a novel Adversarial Training with Domain Adaptation (ATDA) method. Our intuition is to regard the adversarial training on FGSM adversary as a domain adaption task with limited number of target domain samples. The main idea is to learn a representation that is semantically meaningful and domain invariant on the clean domain as well as the adversarial domain. Empirical evaluations on Fashion-MNIST, SVHN, CIFAR-10 and CIFAR-100 demonstrate that ATDA can greatly improve the generalization of adversarial training and the smoothness of the learned models, and outperforms state-of-the-art methods on standard benchmark datasets. To show the transfer ability of our method, we also extend ATDA to the adversarial training on iterative attacks such as PGD-Adversial Training (PAT) and the defense performance is improved considerably.
 The paper casts the problem of learning from adversarial examples to make models resistant to adversarial perturbations to a domain adaptation problem. The proposed method Adversarial training with Domain adapatation( ATDA) learns a representation that is invariant to clean and adversarial data achieving state of the art results on CIFAR. quality - Paper is well written, explanation of the mathematical parts are good, experimental quality can be much better. clarity - the problem motivation as well as the methodology is clearly explained. the learning from the experiments are unclear and need more work. originality - The casting of the problem as domain adaptation is original but from the experiments it was not conclusive as to how much benefit we get. significance of this work - Current models being sensitive to adversarial perturbations is quite a big problem so the particular problem authors are trying to address is very significant. pros A good idea, enough experiments that indicate the benefit of casting this as a domain adaptation problem. cons I feel, the authors should have extended the experiments to ImageNet which is a much larger dataset and validate the findings still hold, I feel the discussion section and comparison to other methods needs to be worked to be more thorough and to tease out the benefit of each of the various terms added to the loss functions as currently all we have is final numbers without much explanation and details. TSNE embeddings part is also very qualitative and while the plots indicate a better separation for ATDA, I feel authors should do more quantitative analysis on the embeddings instead of just qualitative plots.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Improving the Generalization of Adversarial Training with Domain Adaptation
By injecting adversarial examples into training data, adversarial training is promising for improving the robustness of deep learning models. However, most existing adversarial training approaches are based on a specific type of adversarial attack. It may not provide sufficiently representative samples from the adversarial domain, leading to a weak generalization ability on adversarial examples from other attacks. Moreover, during the adversarial training, adversarial perturbations on inputs are usually crafted by fast single-step adversaries so as to scale to large datasets. This work is mainly focused on the adversarial training yet efficient FGSM adversary. In this scenario, it is difficult to train a model with great generalization due to the lack of representative adversarial samples, aka the samples are unable to accurately reflect the adversarial domain. To alleviate this problem, we propose a novel Adversarial Training with Domain Adaptation (ATDA) method. Our intuition is to regard the adversarial training on FGSM adversary as a domain adaption task with limited number of target domain samples. The main idea is to learn a representation that is semantically meaningful and domain invariant on the clean domain as well as the adversarial domain. Empirical evaluations on Fashion-MNIST, SVHN, CIFAR-10 and CIFAR-100 demonstrate that ATDA can greatly improve the generalization of adversarial training and the smoothness of the learned models, and outperforms state-of-the-art methods on standard benchmark datasets. To show the transfer ability of our method, we also extend ATDA to the adversarial training on iterative attacks such as PGD-Adversial Training (PAT) and the defense performance is improved considerably.
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 casts the problem of learning from adversarial examples to make models resistant to adversarial perturbations to a domain adaptation problem. The proposed method Adversarial training with Domain adapatation( ATDA) learns a representation that is invariant to clean and adversarial data achieving state of the art results on CIFAR. quality - Paper is well written, explanation of the mathematical parts are good, experimental quality can be much better. clarity - the problem motivation as well as the methodology is clearly explained. the learning from the experiments are unclear and need more work. originality - The casting of the problem as domain adaptation is original but from the experiments it was not conclusive as to how much benefit we get. significance of this work - Current models being sensitive to adversarial perturbations is quite a big problem so the particular problem authors are trying to address is very significant. pros A good idea, enough experiments that indicate the benefit of casting this as a domain adaptation problem. cons I feel, the authors should have extended the experiments to ImageNet which is a much larger dataset and validate the findings still hold, I feel the discussion section and comparison to other methods needs to be worked to be more thorough and to tease out the benefit of each of the various terms added to the loss functions as currently all we have is final numbers without much explanation and details. TSNE embeddings part is also very qualitative and while the plots indicate a better separation for ATDA, I feel authors should do more quantitative analysis on the embeddings instead of just qualitative plots.
assistant
 The paper casts the problem of learning from adversarial examples to make models resistant to adversarial perturbations to a domain adaptation problem. The proposed method Adversarial training with Domain adapatation( ATDA) learns a representation that is invariant to clean and adversarial data achieving state of the art results on CIFAR. quality - Paper is well written, explanation of the mathematical parts are good, experimental quality can be much better. clarity - the problem motivation as well as the methodology is clearly explained. the learning from the experiments are unclear and need more work. originality - The casting of the problem as domain adaptation is original but from the experiments it was not conclusive as to how much benefit we get. significance of this work - Current models being sensitive to adversarial perturbations is quite a big problem so the particular problem authors are trying to address is very significant. pros A good idea, enough experiments that indicate the benefit of casting this as a domain adaptation problem. cons I feel, the authors should have extended the experiments to ImageNet which is a much larger dataset and validate the findings still hold, I feel the discussion section and comparison to other methods needs to be worked to be more thorough and to tease out the benefit of each of the various terms added to the loss functions as currently all we have is final numbers without much explanation and details. TSNE embeddings part is also very qualitative and while the plots indicate a better separation for ATDA, I feel authors should do more quantitative analysis on the embeddings instead of just qualitative plots.