Domain Adaptation Through Label Propagation: Learning Clustered and Aligned Features
The difficulty of obtaining sufficient labeled data for supervised learning has motivated domain adaptation, in which a classifier is trained in one domain, source domain, but operates in another, target domain. Reducing domain discrepancy has improved the performance, but it is hampered by the embedded features that do not form clearly separable and aligned clusters. We address this issue by propagating labels using a manifold structure, and by enforcing cycle consistency to align the clusters of features in each domain more closely. Specifically, we prove that cycle consistency leads the embedded features distant from all but one clusters if the source domain is ideally clustered. We additionally utilize more information from approximated local manifold and pursue local manifold consistency for more improvement. Results for various domain adaptation scenarios show tighter clustering and an improvement in classification accuracy.
This paper proposes an interesting algorithm towards domain adaptation that combines graph learning and label propagation along with cycle consistency. The proposed model is simple and interesting making clever use of labels in the source domain. While the theory is motivated well and seems convincing, I am not entirely sure of the empirical results. There are no significant improvements of the proposed model over the baselines considered except on STFL-CIFAR data set. An analysis of why this improvement is observed would be good. I also wonder on how did the cluster quality on the target domain compare with that obtained by the AssocDA baseline. Also coming to the parameter sigma in the Gaussian similarity function, it would be nice to see the values of sigma reported in either the body of the paper or in a supplement. I will admit I have not thoroughly checked the math in Theorem 1, but the supporting mathematical equations seem plausible.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Domain Adaptation Through Label Propagation: Learning Clustered and Aligned Features
The difficulty of obtaining sufficient labeled data for supervised learning has motivated domain adaptation, in which a classifier is trained in one domain, source domain, but operates in another, target domain. Reducing domain discrepancy has improved the performance, but it is hampered by the embedded features that do not form clearly separable and aligned clusters. We address this issue by propagating labels using a manifold structure, and by enforcing cycle consistency to align the clusters of features in each domain more closely. Specifically, we prove that cycle consistency leads the embedded features distant from all but one clusters if the source domain is ideally clustered. We additionally utilize more information from approximated local manifold and pursue local manifold consistency for more improvement. Results for various domain adaptation scenarios show tighter clustering and an improvement in classification accuracy.
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 proposes an interesting algorithm towards domain adaptation that combines graph learning and label propagation along with cycle consistency. The proposed model is simple and interesting making clever use of labels in the source domain. While the theory is motivated well and seems convincing, I am not entirely sure of the empirical results. There are no significant improvements of the proposed model over the baselines considered except on STFL-CIFAR data set. An analysis of why this improvement is observed would be good. I also wonder on how did the cluster quality on the target domain compare with that obtained by the AssocDA baseline. Also coming to the parameter sigma in the Gaussian similarity function, it would be nice to see the values of sigma reported in either the body of the paper or in a supplement. I will admit I have not thoroughly checked the math in Theorem 1, but the supporting mathematical equations seem plausible.
assistant
This paper proposes an interesting algorithm towards domain adaptation that combines graph learning and label propagation along with cycle consistency. The proposed model is simple and interesting making clever use of labels in the source domain. While the theory is motivated well and seems convincing, I am not entirely sure of the empirical results. There are no significant improvements of the proposed model over the baselines considered except on STFL-CIFAR data set. An analysis of why this improvement is observed would be good. I also wonder on how did the cluster quality on the target domain compare with that obtained by the AssocDA baseline. Also coming to the parameter sigma in the Gaussian similarity function, it would be nice to see the values of sigma reported in either the body of the paper or in a supplement. I will admit I have not thoroughly checked the math in Theorem 1, but the supporting mathematical equations seem plausible.