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.
 Authors propose a new adversarial training with domain adaptation method to overcome the weak generalisation problem in adversarial training for adversarial examples from different attacks. Authors consider the adversarial training as a domain adaptation task with limited number of target labeled data. They demonstrate that by combining unsupervised and supervised domain adaptation with adversarial training, the generalisation ability on adversarial examples from various attacks can be improved for efficient defence. The experimental results on several benchmark datasets suggest that the proposed approach achieves significantly better generalisation results in most cases, when compared to current competing adversarial training methods. Paper is clearly written and well structured. The novelty of the proposed technique is fair and the originality alike. The results are not very conclusive therefore I think more experiments are needed and possible further adjustments.
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>



 Authors propose a new adversarial training with domain adaptation method to overcome the weak generalisation problem in adversarial training for adversarial examples from different attacks. Authors consider the adversarial training as a domain adaptation task with limited number of target labeled data. They demonstrate that by combining unsupervised and supervised domain adaptation with adversarial training, the generalisation ability on adversarial examples from various attacks can be improved for efficient defence. The experimental results on several benchmark datasets suggest that the proposed approach achieves significantly better generalisation results in most cases, when compared to current competing adversarial training methods. Paper is clearly written and well structured. The novelty of the proposed technique is fair and the originality alike. The results are not very conclusive therefore I think more experiments are needed and possible further adjustments.
assistant
 Authors propose a new adversarial training with domain adaptation method to overcome the weak generalisation problem in adversarial training for adversarial examples from different attacks. Authors consider the adversarial training as a domain adaptation task with limited number of target labeled data. They demonstrate that by combining unsupervised and supervised domain adaptation with adversarial training, the generalisation ability on adversarial examples from various attacks can be improved for efficient defence. The experimental results on several benchmark datasets suggest that the proposed approach achieves significantly better generalisation results in most cases, when compared to current competing adversarial training methods. Paper is clearly written and well structured. The novelty of the proposed technique is fair and the originality alike. The results are not very conclusive therefore I think more experiments are needed and possible further adjustments.