A General Upper Bound for Unsupervised Domain Adaptation
In this work, we present a novel upper bound of target error to address the problem for unsupervised domain adaptation. Recent studies reveal that a deep neural network can learn transferable features which generalize well to novel tasks. Furthermore, Ben-David et al. (2010) provide an upper bound for target error when transferring the knowledge, which can be summarized as minimizing the source error and distance between marginal distributions simultaneously. However, common methods based on the theory usually ignore the joint error such that samples from different classes might be mixed together when matching marginal distribution. And in such case, no matter how we minimize the marginal discrepancy, the target error is not bounded due to an increasing joint error. To address this problem, we propose a general upper bound taking joint error into account, such that the undesirable case can be properly penalized. In addition, we utilize constrained hypothesis space to further formalize a tighter bound as well as a novel cross margin discrepancy to measure the dissimilarity between hypotheses which alleviates instability during adversarial learning. Extensive empirical evidence shows that our proposal outperforms related approaches in image classification error rates on standard domain adaptation benchmarks.
The authors propose an approach based on an upper bound on target domain error for the task of unsupervised domain adaptation (where one does not have access to any labels in the target domain). The upper bound makes possible the penalization of mixing samples from different classes together during distribution matching. Empirical results on image classification in a few benchmarks show the significant promise of the approach. The paper makes a good attempt at explaining the ideas/concepts, but still remains very unpolished (including English usage issues), and it is hard to follow in a number of places. A few such, a sampling, are mentioned below with some suggestions. In my opinion, not ready for publication. * abstract: *.. to address the problem for unsupervised domain adaptation.* >> *to address a major problem facing many unsupervised domain adaptation techniques*. * page 2, several rewordings in: *The reason is obvious, as marginal distributions being matched for source and target, it is possible that samples from different classes are aligned together, where the joint error becomes non-negligible since no hypothesis can classify source and target at the same time.* For instance, a partial rewording: *.. when an attempt is made to match marginal distributions of source and target domains, samples from different classes can be mixed together*. Also, I wouldn*t use *The reason is obvious*... Finally, here it is a good place to refer to Figure 1. * page 2: *our proposal can degrade to some other methods* >> *.. can reduce to several other methods ..* * in lines 1 and 2 on pg 3, the simplification from line 1 to 2, explain the major reasoning (perhaps in the appendix if you don*t have space): there are number of terms added and subtracted (which is understandable), but hard to keep track of what simplifications are being carried and where the terms move (too many terms)... * *the following theorem holds* >> *the following bound holds* (or inequality, etc.) * Is derivation 3 from triangle inequality? (add that explanation to line 3) * hard to parse (missing pronoun): *the above upper bound in minimized when h=f_s thus equivalent to ..* * *is capable of* >> *is capable to do so* (and at this point, is not yet clear how the bound helps avoid the problems with distribution matching.. ) * Figure 1 (and subsequent figures): suggest put A, B and A* and B* for the two classes and domains, inside the circles, so it*s easier to see what the source and target domain classes are (dotted boundary is hard to discern). * on top of pg 4: couldn*t the feature extractor make the max-player stronger too? ( In *... since the max-player taking two parameters f1 , f2 is too strong, we introduce a feature extractor g to make the min-player stronger.. * .. )
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
A General Upper Bound for Unsupervised Domain Adaptation
In this work, we present a novel upper bound of target error to address the problem for unsupervised domain adaptation. Recent studies reveal that a deep neural network can learn transferable features which generalize well to novel tasks. Furthermore, Ben-David et al. (2010) provide an upper bound for target error when transferring the knowledge, which can be summarized as minimizing the source error and distance between marginal distributions simultaneously. However, common methods based on the theory usually ignore the joint error such that samples from different classes might be mixed together when matching marginal distribution. And in such case, no matter how we minimize the marginal discrepancy, the target error is not bounded due to an increasing joint error. To address this problem, we propose a general upper bound taking joint error into account, such that the undesirable case can be properly penalized. In addition, we utilize constrained hypothesis space to further formalize a tighter bound as well as a novel cross margin discrepancy to measure the dissimilarity between hypotheses which alleviates instability during adversarial learning. Extensive empirical evidence shows that our proposal outperforms related approaches in image classification error rates on standard domain adaptation 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 authors propose an approach based on an upper bound on target domain error for the task of unsupervised domain adaptation (where one does not have access to any labels in the target domain). The upper bound makes possible the penalization of mixing samples from different classes together during distribution matching. Empirical results on image classification in a few benchmarks show the significant promise of the approach. The paper makes a good attempt at explaining the ideas/concepts, but still remains very unpolished (including English usage issues), and it is hard to follow in a number of places. A few such, a sampling, are mentioned below with some suggestions. In my opinion, not ready for publication. * abstract: *.. to address the problem for unsupervised domain adaptation.* >> *to address a major problem facing many unsupervised domain adaptation techniques*. * page 2, several rewordings in: *The reason is obvious, as marginal distributions being matched for source and target, it is possible that samples from different classes are aligned together, where the joint error becomes non-negligible since no hypothesis can classify source and target at the same time.* For instance, a partial rewording: *.. when an attempt is made to match marginal distributions of source and target domains, samples from different classes can be mixed together*. Also, I wouldn*t use *The reason is obvious*... Finally, here it is a good place to refer to Figure 1. * page 2: *our proposal can degrade to some other methods* >> *.. can reduce to several other methods ..* * in lines 1 and 2 on pg 3, the simplification from line 1 to 2, explain the major reasoning (perhaps in the appendix if you don*t have space): there are number of terms added and subtracted (which is understandable), but hard to keep track of what simplifications are being carried and where the terms move (too many terms)... * *the following theorem holds* >> *the following bound holds* (or inequality, etc.) * Is derivation 3 from triangle inequality? (add that explanation to line 3) * hard to parse (missing pronoun): *the above upper bound in minimized when h=f_s thus equivalent to ..* * *is capable of* >> *is capable to do so* (and at this point, is not yet clear how the bound helps avoid the problems with distribution matching.. ) * Figure 1 (and subsequent figures): suggest put A, B and A* and B* for the two classes and domains, inside the circles, so it*s easier to see what the source and target domain classes are (dotted boundary is hard to discern). * on top of pg 4: couldn*t the feature extractor make the max-player stronger too? ( In *... since the max-player taking two parameters f1 , f2 is too strong, we introduce a feature extractor g to make the min-player stronger.. * .. )
assistant
The authors propose an approach based on an upper bound on target domain error for the task of unsupervised domain adaptation (where one does not have access to any labels in the target domain). The upper bound makes possible the penalization of mixing samples from different classes together during distribution matching. Empirical results on image classification in a few benchmarks show the significant promise of the approach. The paper makes a good attempt at explaining the ideas/concepts, but still remains very unpolished (including English usage issues), and it is hard to follow in a number of places. A few such, a sampling, are mentioned below with some suggestions. In my opinion, not ready for publication. * abstract: *.. to address the problem for unsupervised domain adaptation. * >> *to address a major problem facing many unsupervised domain adaptation techniques*. * page 2, several rewordings in: *The reason is obvious, as marginal distributions being matched for source and target, it is possible that samples from different classes are aligned together, where the joint error becomes non-negligible since no hypothesis can classify source and target at the same time. * For instance, a partial rewording: *.. when an attempt is made to match marginal distributions of source and target domains, samples from different classes can be mixed together*. Also, I wouldn*t use *The reason is obvious*... Finally, here it is a good place to refer to Figure 1. * page 2: *our proposal can degrade to some other methods* >> *.. can reduce to several other methods ..* * in lines 1 and 2 on pg 3, the simplification from line 1 to 2, explain the major reasoning (perhaps in the appendix if you don*t have space): there are number of terms added and subtracted (which is understandable), but hard to keep track of what simplifications are being carried and where the terms move (too many terms)... * *the following theorem holds* >> *the following bound holds* (or inequality, etc.) * Is derivation 3 from triangle inequality? (add that explanation to line 3) * hard to parse (missing pronoun): *the above upper bound in minimized when h=f_s thus equivalent to ..* * *is capable of* >> *is capable to do so* (and at this point, is not yet clear how the bound helps avoid the problems with distribution matching.. ) * Figure 1 (and subsequent figures): suggest put A, B and A* and B* for the two classes and domains, inside the circles, so it*s easier to see what the source and target domain classes are (dotted boundary is hard to discern). * on top of pg 4: couldn*t the feature extractor make the max-player stronger too? ( In *... since the max-player taking two parameters f1 , f2 is too strong, we introduce a feature extractor g to make the min-player stronger.. * .. )