LEARNING FACTORIZED REPRESENTATIONS FOR OPEN-SET DOMAIN ADAPTATION
Domain adaptation for visual recognition has undergone great progress in the past few years. Nevertheless, most existing methods work in the so-called closed-set scenario, assuming that the classes depicted by the target images are exactly the same as those of the source domain. In this paper, we tackle the more challenging, yet more realistic case of open-set domain adaptation, where new, unknown classes can be present in the target data. While, in the unsupervised scenario, one cannot expect to be able to identify each specific new class, we aim to automatically detect which samples belong to these new classes and discard them from the recognition process. To this end, we rely on the intuition that the source and target samples depicting the known classes can be generated by a shared subspace, whereas the target samples from unknown classes come from a different, private subspace. We therefore introduce a framework that factorizes the data into shared and private parts, while encouraging the shared representation to be discriminative. Our experiments on standard benchmarks evidence that our approach significantly outperforms the state-of-the-art in open-set domain adaptation.
 This paper tackles the problem of open-set unsupervised domain adaptation with a method based on subspace learning. Specifically the proposed approach searches for two low-dimensional spaces, one shared by the known source and target categories while the other is specific for the unknown classes. Overall the paper is well organized and easy to read. The mathematical formulation of the method is sound and clearly explained in all its variants. I have few concerns - it would be good to have the "average" columns in the tables reporting the experimental results. This will help to have an overall idea on the performance of the different proposed and baseline methods. - it is not clear whether the authors are reporting the results of AODA from the original paper or if they re-ran the code to get the recognition accuracies. For instance in table 3 the result 70.1 for A->W is lower than those reported in the original paper for this setting. - the paper does not discuss how the hyperparameters of the methods are chosen. Only an analysis on epsilon is provided. It would be very helpful to understand the procedure used to select the values of alpha, beta and lambda and to evaluate the robustness of the method to those parameters. Moreover, the value of the dimensionality d is not explicitly indicated in the text. This should be added together with a discussion about if and how the subspace disagreement measure (that was introduced for closed set domain adaptation) is reliable in the open set condition.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
LEARNING FACTORIZED REPRESENTATIONS FOR OPEN-SET DOMAIN ADAPTATION
Domain adaptation for visual recognition has undergone great progress in the past few years. Nevertheless, most existing methods work in the so-called closed-set scenario, assuming that the classes depicted by the target images are exactly the same as those of the source domain. In this paper, we tackle the more challenging, yet more realistic case of open-set domain adaptation, where new, unknown classes can be present in the target data. While, in the unsupervised scenario, one cannot expect to be able to identify each specific new class, we aim to automatically detect which samples belong to these new classes and discard them from the recognition process. To this end, we rely on the intuition that the source and target samples depicting the known classes can be generated by a shared subspace, whereas the target samples from unknown classes come from a different, private subspace. We therefore introduce a framework that factorizes the data into shared and private parts, while encouraging the shared representation to be discriminative. Our experiments on standard benchmarks evidence that our approach significantly outperforms the state-of-the-art in open-set domain adaptation.
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>



 This paper tackles the problem of open-set unsupervised domain adaptation with a method based on subspace learning. Specifically the proposed approach searches for two low-dimensional spaces, one shared by the known source and target categories while the other is specific for the unknown classes. Overall the paper is well organized and easy to read. The mathematical formulation of the method is sound and clearly explained in all its variants. I have few concerns - it would be good to have the "average" columns in the tables reporting the experimental results. This will help to have an overall idea on the performance of the different proposed and baseline methods. - it is not clear whether the authors are reporting the results of AODA from the original paper or if they re-ran the code to get the recognition accuracies. For instance in table 3 the result 70.1 for A->W is lower than those reported in the original paper for this setting. - the paper does not discuss how the hyperparameters of the methods are chosen. Only an analysis on epsilon is provided. It would be very helpful to understand the procedure used to select the values of alpha, beta and lambda and to evaluate the robustness of the method to those parameters. Moreover, the value of the dimensionality d is not explicitly indicated in the text. This should be added together with a discussion about if and how the subspace disagreement measure (that was introduced for closed set domain adaptation) is reliable in the open set condition.
assistant
 This paper tackles the problem of open-set unsupervised domain adaptation with a method based on subspace learning. Specifically the proposed approach searches for two low-dimensional spaces, one shared by the known source and target categories while the other is specific for the unknown classes. Overall the paper is well organized and easy to read. The mathematical formulation of the method is sound and clearly explained in all its variants. I have few concerns - it would be good to have the "average" columns in the tables reporting the experimental results. This will help to have an overall idea on the performance of the different proposed and baseline methods. - it is not clear whether the authors are reporting the results of AODA from the original paper or if they re-ran the code to get the recognition accuracies. For instance in table 3 the result 70.1 for A->W is lower than those reported in the original paper for this setting. - the paper does not discuss how the hyperparameters of the methods are chosen. Only an analysis on epsilon is provided. It would be very helpful to understand the procedure used to select the values of alpha, beta and lambda and to evaluate the robustness of the method to those parameters. Moreover, the value of the dimensionality d is not explicitly indicated in the text. This should be added together with a discussion about if and how the subspace disagreement measure (that was introduced for closed set domain adaptation) is reliable in the open set condition.