Domain Adaptive Transfer Learning
Transfer learning is a widely used method to build high performing computer vision models. In this paper, we study the efficacy of transfer learning by examining how the choice of data impacts performance. We find that more pre-training data does not always help, and transfer performance depends on a judicious choice of pre-training data. These findings are important given the continued increase in dataset sizes. We further propose domain adaptive transfer learning, a simple and effective pre-training method using importance weights computed based on the target dataset. Our methods achieve state-of-the-art results on multiple fine-grained classification datasets and are well-suited for use in practice.
 This paper is of limited technical contribution. The proposed method in Section 3.3 is just too close to covariate shift where importance weighting has been widely used. I don’t know whether authors are aware of these works. The findings listed in Section 1.1 are obvious and intuitive. There is no interesting finding. It is better to move the introduction of the datasets to the experimental section. A typo: hand-curated -> handcrafted
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Domain Adaptive Transfer Learning
Transfer learning is a widely used method to build high performing computer vision models. In this paper, we study the efficacy of transfer learning by examining how the choice of data impacts performance. We find that more pre-training data does not always help, and transfer performance depends on a judicious choice of pre-training data. These findings are important given the continued increase in dataset sizes. We further propose domain adaptive transfer learning, a simple and effective pre-training method using importance weights computed based on the target dataset. Our methods achieve state-of-the-art results on multiple fine-grained classification datasets and are well-suited for use in practice.
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 is of limited technical contribution. The proposed method in Section 3.3 is just too close to covariate shift where importance weighting has been widely used. I don’t know whether authors are aware of these works. The findings listed in Section 1.1 are obvious and intuitive. There is no interesting finding. It is better to move the introduction of the datasets to the experimental section. A typo: hand-curated -> handcrafted
assistant
 This paper is of limited technical contribution. The proposed method in Section 3.3 is just too close to covariate shift where importance weighting has been widely used. I don’t know whether authors are aware of these works. The findings listed in Section 1.1 are obvious and intuitive. There is no interesting finding. It is better to move the introduction of the datasets to the experimental section. A typo: hand-curated -> handcrafted