Universal Source-Free Domain Adaptation
There is a strong incentive to develop versatile learning techniques that can transfer the knowledge of class-separability from a labeled source domain to an unlabeled target domain in the presence of a domain-shift. Existing domain adaptation (DA) approaches are not equipped for practical DA scenarios as a result of their reliance on the knowledge of source-target label-set relationship (e.g. Closed-set, Open-set or Partial DA). Furthermore, almost all the prior unsupervised DA works require coexistence of source and target samples even during deployment, making them unsuitable for incremental, real-time adaptation. Devoid of such highly impractical assumptions, we propose a novel two-stage learning process. Initially, in the procurement-stage, the objective is to equip the model for future source-free deployment, assuming no prior knowledge of the upcoming category-gap and domain-shift. To achieve this, we enhance the model’s ability to reject out-of-source distribution samples by leveraging the available source data, in a novel generative classifier framework. Subsequently, in the deployment-stage, the objective is to design a unified adaptation algorithm capable of operating across a wide range of category-gaps, with no access to the previously seen source samples. To achieve this, in contrast to the usage of complex adversarial training regimes, we define a simple yet effective source-free adaptation objective by utilizing a novel instance-level weighing mechanism, named as Source Similarity Metric (SSM). A thorough evaluation shows the practical usability of the proposed learning framework with superior DA performance even over state-of-the-art source-dependent approaches.
This interesting paper on an important topic is extremely hard to read because it assumes that the reader is deeply familiar with the Domain Adaptation. In order to make the paper more accessible to a general audience, the authors should: 1) follow the 1st sentence in the abstract by a new sentence that provides an intuitive example the idea of domain shift from labeled source to unlabeled target domain 2) re-organize the current intro, which reads more like related work, into a more traditional format - one intuitive paragraph about domain adaptation, source/target domain, and domain shift; provide at least one compelling illustrative example from a real-world application - one intuitive paragraph with an illustrative example on how the proposed approach will help solve a problem; at the same intuitive level, compare-and-contrast it with existing approaches - one intuitive, in-detail paragraph on how the proposed approach works - one paragraph summarizing the main findings/results 3) ideally, add a section with an illustrative running example, which would have a huge impact on the paper*s readability
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Universal Source-Free Domain Adaptation
There is a strong incentive to develop versatile learning techniques that can transfer the knowledge of class-separability from a labeled source domain to an unlabeled target domain in the presence of a domain-shift. Existing domain adaptation (DA) approaches are not equipped for practical DA scenarios as a result of their reliance on the knowledge of source-target label-set relationship (e.g. Closed-set, Open-set or Partial DA). Furthermore, almost all the prior unsupervised DA works require coexistence of source and target samples even during deployment, making them unsuitable for incremental, real-time adaptation. Devoid of such highly impractical assumptions, we propose a novel two-stage learning process. Initially, in the procurement-stage, the objective is to equip the model for future source-free deployment, assuming no prior knowledge of the upcoming category-gap and domain-shift. To achieve this, we enhance the model’s ability to reject out-of-source distribution samples by leveraging the available source data, in a novel generative classifier framework. Subsequently, in the deployment-stage, the objective is to design a unified adaptation algorithm capable of operating across a wide range of category-gaps, with no access to the previously seen source samples. To achieve this, in contrast to the usage of complex adversarial training regimes, we define a simple yet effective source-free adaptation objective by utilizing a novel instance-level weighing mechanism, named as Source Similarity Metric (SSM). A thorough evaluation shows the practical usability of the proposed learning framework with superior DA performance even over state-of-the-art source-dependent approaches.
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 interesting paper on an important topic is extremely hard to read because it assumes that the reader is deeply familiar with the Domain Adaptation. In order to make the paper more accessible to a general audience, the authors should: 1) follow the 1st sentence in the abstract by a new sentence that provides an intuitive example the idea of domain shift from labeled source to unlabeled target domain 2) re-organize the current intro, which reads more like related work, into a more traditional format - one intuitive paragraph about domain adaptation, source/target domain, and domain shift; provide at least one compelling illustrative example from a real-world application - one intuitive paragraph with an illustrative example on how the proposed approach will help solve a problem; at the same intuitive level, compare-and-contrast it with existing approaches - one intuitive, in-detail paragraph on how the proposed approach works - one paragraph summarizing the main findings/results 3) ideally, add a section with an illustrative running example, which would have a huge impact on the paper*s readability
assistant
This interesting paper on an important topic is extremely hard to read because it assumes that the reader is deeply familiar with the Domain Adaptation. In order to make the paper more accessible to a general audience, the authors should: 1) follow the 1st sentence in the abstract by a new sentence that provides an intuitive example the idea of domain shift from labeled source to unlabeled target domain 2) re-organize the current intro, which reads more like related work, into a more traditional format - one intuitive paragraph about domain adaptation, source/target domain, and domain shift; provide at least one compelling illustrative example from a real-world application - one intuitive paragraph with an illustrative example on how the proposed approach will help solve a problem; at the same intuitive level, compare-and-contrast it with existing approaches - one intuitive, in-detail paragraph on how the proposed approach works - one paragraph summarizing the main findings/results 3) ideally, add a section with an illustrative running example, which would have a huge impact on the paper*s readability