Generalizing Across Domains via Cross-Gradient Training
We present CROSSGRAD , a method to use multi-domain training data to learn a classifier that generalizes to new domains. CROSSGRAD does not need an adaptation phase via labeled or unlabeled data, or domain features in the new domain. Most existing domain adaptation methods attempt to erase domain signals using techniques like domain adversarial training. In contrast, CROSSGRAD is free to use domain signals for predicting labels, if it can prevent overfitting on training domains. We conceptualize the task in a Bayesian setting, in which a sampling step is implemented as data augmentation, based on domain-guided perturbations of input instances. CROSSGRAD jointly trains a label and a domain classifier on examples perturbed by loss gradients of each other’s objectives. This enables us to directly perturb inputs, without separating and re-mixing domain signals while making various distributional assumptions. Empirical evaluation on three different applications where this setting is natural establishes that (1) domain-guided perturbation provides consistently better generalization to unseen domains, compared to generic instance perturbation methods, and (2) data augmentation is a more stable and accurate method than domain adversarial training.
 Quality, clarity : Very well written, well motivated, convincing experiments and analysis Originality: I think they framed the problem of domain-robustness very well: how to obtain a "domain level embedding" which generalizes to unseen domains. To do this the authors introduce the CrossGrad method, which trains both a label classification task and a domain classification task (from which the domain-embedding is obtained) Significance: Robustness in new domains is a very important practical and theoretical issue. Pros: - It*s novel, interesting, well written, and appears to work very well in the experiments provided. Cons: - Formally, for the embedding to generalize one needs to make the "domain continuity assumption", which is not guaranteed to hold in any realistic settings (e.g. when there are no underlying continuous factors) - The training set needs to be in the form (x,y,d) where *d* is a domain, this information might not exist or be only partially present. - A single step required 2 forward and 2 backward passes - thus is twice as expensive. Constructive comments: - Algorithm 1 uses both X_l and X_d, yet the text only discusses X_d, there is some symmetry, but more discussion will help. - LabelGrad is mentioned in section 4 but defined in section 4.1, it should be briefly defined in the first mention.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Generalizing Across Domains via Cross-Gradient Training
We present CROSSGRAD , a method to use multi-domain training data to learn a classifier that generalizes to new domains. CROSSGRAD does not need an adaptation phase via labeled or unlabeled data, or domain features in the new domain. Most existing domain adaptation methods attempt to erase domain signals using techniques like domain adversarial training. In contrast, CROSSGRAD is free to use domain signals for predicting labels, if it can prevent overfitting on training domains. We conceptualize the task in a Bayesian setting, in which a sampling step is implemented as data augmentation, based on domain-guided perturbations of input instances. CROSSGRAD jointly trains a label and a domain classifier on examples perturbed by loss gradients of each other’s objectives. This enables us to directly perturb inputs, without separating and re-mixing domain signals while making various distributional assumptions. Empirical evaluation on three different applications where this setting is natural establishes that (1) domain-guided perturbation provides consistently better generalization to unseen domains, compared to generic instance perturbation methods, and (2) data augmentation is a more stable and accurate method than domain adversarial training.
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>



 Quality, clarity : Very well written, well motivated, convincing experiments and analysis Originality: I think they framed the problem of domain-robustness very well: how to obtain a "domain level embedding" which generalizes to unseen domains. To do this the authors introduce the CrossGrad method, which trains both a label classification task and a domain classification task (from which the domain-embedding is obtained) Significance: Robustness in new domains is a very important practical and theoretical issue. Pros: - It*s novel, interesting, well written, and appears to work very well in the experiments provided. Cons: - Formally, for the embedding to generalize one needs to make the "domain continuity assumption", which is not guaranteed to hold in any realistic settings (e.g. when there are no underlying continuous factors) - The training set needs to be in the form (x,y,d) where *d* is a domain, this information might not exist or be only partially present. - A single step required 2 forward and 2 backward passes - thus is twice as expensive. Constructive comments: - Algorithm 1 uses both X_l and X_d, yet the text only discusses X_d, there is some symmetry, but more discussion will help. - LabelGrad is mentioned in section 4 but defined in section 4.1, it should be briefly defined in the first mention.
assistant
 Quality, clarity : Very well written, well motivated, convincing experiments and analysis Originality: I think they framed the problem of domain-robustness very well: how to obtain a "domain level embedding" which generalizes to unseen domains. To do this the authors introduce the CrossGrad method, which trains both a label classification task and a domain classification task (from which the domain-embedding is obtained) Significance: Robustness in new domains is a very important practical and theoretical issue. Pros: - It*s novel, interesting, well written, and appears to work very well in the experiments provided. Cons: - Formally, for the embedding to generalize one needs to make the "domain continuity assumption", which is not guaranteed to hold in any realistic settings (e.g. when there are no underlying continuous factors) - The training set needs to be in the form (x,y,d) where *d* is a domain, this information might not exist or be only partially present. - A single step required 2 forward and 2 backward passes - thus is twice as expensive. Constructive comments: - Algorithm 1 uses both X_l and X_d, yet the text only discusses X_d, there is some symmetry, but more discussion will help. - LabelGrad is mentioned in section 4 but defined in section 4.1, it should be briefly defined in the first mention.