Generalized Domain Adaptation with Covariate and Label Shift CO-ALignment
Unsupervised knowledge transfer has a great potential to improve the generalizability of deep models to novel domains. Yet the current literature assumes that the label distribution is domain-invariant and only aligns the covariate or vice versa. In this paper, we explore the task of Generalized Domain Adaptation (GDA): How to transfer knowledge across different domains in the presence of both covariate and label shift? We propose a covariate and label distribution CO-ALignment (COAL) model to tackle this problem. Our model leverages prototype-based conditional alignment and label distribution estimation to diminish the covariate and label shifts, respectively. We demonstrate experimentally that when both types of shift exist in the data, COAL leads to state-of-the-art performance on several cross-domain benchmarks.
The paper deals with covariate and label shift in common evaluated on some standard benchmark data. The paper is scientific sound and appears to be in good shape, just a few comments: - how is your label shift different to concept drift in supervised learning? --> if this is basically the same I would expect that you can use,mention methods from there - the plots with t-SNE are obviously colorful but do not provide a lot of information - they should be removed - I do not see a benefit - it is very common that all these methods (like yours) are provided on some kind of image data ... is there a particular reason / limitation? - I would like to see additional experiments on similar text data -> reuters - self-training is a concept from semi-supervised learning and not particular well supported in the community - how do you make sure that the result remain valid - how do you make sure that the adaptation of the labels, the covariate shift and the classifier training are not in facting cheating the result to an optimum within the optimized cost function?
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Generalized Domain Adaptation with Covariate and Label Shift CO-ALignment
Unsupervised knowledge transfer has a great potential to improve the generalizability of deep models to novel domains. Yet the current literature assumes that the label distribution is domain-invariant and only aligns the covariate or vice versa. In this paper, we explore the task of Generalized Domain Adaptation (GDA): How to transfer knowledge across different domains in the presence of both covariate and label shift? We propose a covariate and label distribution CO-ALignment (COAL) model to tackle this problem. Our model leverages prototype-based conditional alignment and label distribution estimation to diminish the covariate and label shifts, respectively. We demonstrate experimentally that when both types of shift exist in the data, COAL leads to state-of-the-art performance on several cross-domain benchmarks.
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 deals with covariate and label shift in common evaluated on some standard benchmark data. The paper is scientific sound and appears to be in good shape, just a few comments: - how is your label shift different to concept drift in supervised learning? --> if this is basically the same I would expect that you can use,mention methods from there - the plots with t-SNE are obviously colorful but do not provide a lot of information - they should be removed - I do not see a benefit - it is very common that all these methods (like yours) are provided on some kind of image data ... is there a particular reason / limitation? - I would like to see additional experiments on similar text data -> reuters - self-training is a concept from semi-supervised learning and not particular well supported in the community - how do you make sure that the result remain valid - how do you make sure that the adaptation of the labels, the covariate shift and the classifier training are not in facting cheating the result to an optimum within the optimized cost function?
assistant
The paper deals with covariate and label shift in common evaluated on some standard benchmark data. The paper is scientific sound and appears to be in good shape, just a few comments: - how is your label shift different to concept drift in supervised learning? --> if this is basically the same I would expect that you can use,mention methods from there - the plots with t-SNE are obviously colorful but do not provide a lot of information - they should be removed - I do not see a benefit - it is very common that all these methods (like yours) are provided on some kind of image data ... is there a particular reason / limitation? - I would like to see additional experiments on similar text data -> reuters - self-training is a concept from semi-supervised learning and not particular well supported in the community - how do you make sure that the result remain valid - how do you make sure that the adaptation of the labels, the covariate shift and the classifier training are not in facting cheating the result to an optimum within the optimized cost function?