Domain Adaptive Multiflow Networks
We tackle unsupervised domain adaptation by accounting for the fact that different domains may need to be processed differently to arrive to a common feature representation effective for recognition. To this end, we introduce a deep learning framework where each domain undergoes a different sequence of operations, allowing some, possibly more complex, domains to go through more computations than others. This contrasts with state-of-the-art domain adaptation techniques that force all domains to be processed with the same series of operations, even when using multi-stream architectures whose parameters are not shared. As evidenced by our experiments, the greater flexibility of our method translates to higher accuracy. Furthermore, it allows us to handle any number of domains simultaneously.
The paper provides an unsupervised domain adaptation approach in the context of deep learning. The motivation is clear, related work sufficient and experimental settings and results convincing. I have only very minor comments: - I would prefer to get the paper additionally linked to a few more transfer learning techniques out of the deep learning domain which is important as well - do you really need to call it (multi) flow network .... - a flow network is a well established concept in algorithmics and refers to a graph problem ... to avoid name clashes ... - in the references you have provided back links to the pages where the references are used - this is handy but also confusing and a bit unusual - I think it was not part of the standard template - please avoid using arxiv references but replace them by reviewed material. In parts I am willing to accept such kind of gray literature provided by well known authors but this should not become a standard habit - I am happy to see that the code will be published - I hope this is really done, because from the material it maybe hard to reconstruct the method
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Domain Adaptive Multiflow Networks
We tackle unsupervised domain adaptation by accounting for the fact that different domains may need to be processed differently to arrive to a common feature representation effective for recognition. To this end, we introduce a deep learning framework where each domain undergoes a different sequence of operations, allowing some, possibly more complex, domains to go through more computations than others. This contrasts with state-of-the-art domain adaptation techniques that force all domains to be processed with the same series of operations, even when using multi-stream architectures whose parameters are not shared. As evidenced by our experiments, the greater flexibility of our method translates to higher accuracy. Furthermore, it allows us to handle any number of domains simultaneously.
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 provides an unsupervised domain adaptation approach in the context of deep learning. The motivation is clear, related work sufficient and experimental settings and results convincing. I have only very minor comments: - I would prefer to get the paper additionally linked to a few more transfer learning techniques out of the deep learning domain which is important as well - do you really need to call it (multi) flow network .... - a flow network is a well established concept in algorithmics and refers to a graph problem ... to avoid name clashes ... - in the references you have provided back links to the pages where the references are used - this is handy but also confusing and a bit unusual - I think it was not part of the standard template - please avoid using arxiv references but replace them by reviewed material. In parts I am willing to accept such kind of gray literature provided by well known authors but this should not become a standard habit - I am happy to see that the code will be published - I hope this is really done, because from the material it maybe hard to reconstruct the method
assistant
The paper provides an unsupervised domain adaptation approach in the context of deep learning. The motivation is clear, related work sufficient and experimental settings and results convincing. I have only very minor comments: - I would prefer to get the paper additionally linked to a few more transfer learning techniques out of the deep learning domain which is important as well - do you really need to call it (multi) flow network .... - a flow network is a well established concept in algorithmics and refers to a graph problem ... to avoid name clashes ... - in the references you have provided back links to the pages where the references are used - this is handy but also confusing and a bit unusual - I think it was not part of the standard template - please avoid using arxiv references but replace them by reviewed material. In parts I am willing to accept such kind of gray literature provided by well known authors but this should not become a standard habit - I am happy to see that the code will be published - I hope this is really done, because from the material it maybe hard to reconstruct the method