Wildly Unsupervised Domain Adaptation and Its Powerful and Efficient Solution
In unsupervised domain adaptation (UDA), classifiers for the target domain (TD) are trained with clean labeled data from the source domain (SD) and unlabeled data from TD. However, in the wild, it is hard to acquire a large amount of perfectly clean labeled data in SD given limited budget. Hence, we consider a new, more realistic and more challenging problem setting, where classifiers have to be trained with noisy labeled data from SD and unlabeled data from TD---we name it wildly UDA (WUDA). We show that WUDA ruins all UDA methods if taking no care of label noise in SD, and to this end, we propose a Butterfly framework, a powerful and efficient solution to WUDA. Butterfly maintains four models (e.g., deep networks) simultaneously, where two take care of all adaptations (i.e., noisy-to-clean, labeled-to-unlabeled, and SD-to-TD-distributional) and then the other two can focus on classification in TD. As a consequence, Butterfly possesses all the conceptually necessary components for solving WUDA. Experiments demonstrate that under WUDA, Butterfly significantly outperforms existing baseline methods.
This paper presents a method for unsupervised domain adaptation. The problem is well known in literature and follows the setting of labeled source and unlabeled target set. This work proposes the “butterfly network” suitable to train on noisy data (labels) and assign pseudo-labels to the target set. The butterfly network consists in two branches one for source+target and one for target only data. Both use the same optimization objective and a “checking” mechanism has been devised for pseudo-labelling the data. Decision: weak reject Motivation: This paper has some merit but does not present the method in a clear way, it requires some additional effort to go through the method and retrieve from the appendix information useful for full understanding. For example algorithm 2 is a key component of the method and is in appendix, notation is not always clear nor explained (the loss in algorithm 2 is ), the networks F_1, F_2, F_t1, F_t2 could be added in figure 3 for more clear understanding and R(t) along with u_i could be more clearly defined (how to obtain u_i is still not clear to me after several re-reading of the paper). The introduced losses should be better justified. Anyway, as I said the paper has some merit, it provides many insights and extensive analysis on “butterfly” method for unsupervised domain adaptation. The experimental section is extensive and demonstrates improvement in performance using this method compared to state-of-the-art, even though for some *real world* datasets (e.g. SUN, Caltech, ImageNet) the improvement is not so significant as in the case of MNIST-SYND. Could the authors provide results on MNIST-SVHN to help compare with other papers in literature? Did the authors observe the same evolution of the accuracy (decreasing and increasing after a few epochs) also on the *real world* datasets as in MNIST-SYND? Replicability: as I said the method is not really clearly explained and therefore I am not confident I could implement and replicate the results. This not because I think the method is complex but because some key components that I pointed out previously about the method are not clear and I strogly believe these are key components to replicate the results.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Wildly Unsupervised Domain Adaptation and Its Powerful and Efficient Solution
In unsupervised domain adaptation (UDA), classifiers for the target domain (TD) are trained with clean labeled data from the source domain (SD) and unlabeled data from TD. However, in the wild, it is hard to acquire a large amount of perfectly clean labeled data in SD given limited budget. Hence, we consider a new, more realistic and more challenging problem setting, where classifiers have to be trained with noisy labeled data from SD and unlabeled data from TD---we name it wildly UDA (WUDA). We show that WUDA ruins all UDA methods if taking no care of label noise in SD, and to this end, we propose a Butterfly framework, a powerful and efficient solution to WUDA. Butterfly maintains four models (e.g., deep networks) simultaneously, where two take care of all adaptations (i.e., noisy-to-clean, labeled-to-unlabeled, and SD-to-TD-distributional) and then the other two can focus on classification in TD. As a consequence, Butterfly possesses all the conceptually necessary components for solving WUDA. Experiments demonstrate that under WUDA, Butterfly significantly outperforms existing baseline methods.
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 presents a method for unsupervised domain adaptation. The problem is well known in literature and follows the setting of labeled source and unlabeled target set. This work proposes the “butterfly network” suitable to train on noisy data (labels) and assign pseudo-labels to the target set. The butterfly network consists in two branches one for source+target and one for target only data. Both use the same optimization objective and a “checking” mechanism has been devised for pseudo-labelling the data. Decision: weak reject Motivation: This paper has some merit but does not present the method in a clear way, it requires some additional effort to go through the method and retrieve from the appendix information useful for full understanding. For example algorithm 2 is a key component of the method and is in appendix, notation is not always clear nor explained (the loss in algorithm 2 is ), the networks F_1, F_2, F_t1, F_t2 could be added in figure 3 for more clear understanding and R(t) along with u_i could be more clearly defined (how to obtain u_i is still not clear to me after several re-reading of the paper). The introduced losses should be better justified. Anyway, as I said the paper has some merit, it provides many insights and extensive analysis on “butterfly” method for unsupervised domain adaptation. The experimental section is extensive and demonstrates improvement in performance using this method compared to state-of-the-art, even though for some *real world* datasets (e.g. SUN, Caltech, ImageNet) the improvement is not so significant as in the case of MNIST-SYND. Could the authors provide results on MNIST-SVHN to help compare with other papers in literature? Did the authors observe the same evolution of the accuracy (decreasing and increasing after a few epochs) also on the *real world* datasets as in MNIST-SYND? Replicability: as I said the method is not really clearly explained and therefore I am not confident I could implement and replicate the results. This not because I think the method is complex but because some key components that I pointed out previously about the method are not clear and I strogly believe these are key components to replicate the results.
assistant
This paper presents a method for unsupervised domain adaptation. The problem is well known in literature and follows the setting of labeled source and unlabeled target set. This work proposes the “butterfly network” suitable to train on noisy data (labels) and assign pseudo-labels to the target set. The butterfly network consists in two branches one for source+target and one for target only data. Both use the same optimization objective and a “checking” mechanism has been devised for pseudo-labelling the data. Decision: weak reject Motivation: This paper has some merit but does not present the method in a clear way, it requires some additional effort to go through the method and retrieve from the appendix information useful for full understanding. For example algorithm 2 is a key component of the method and is in appendix, notation is not always clear nor explained (the loss in algorithm 2 is ), the networks F_1, F_2, F_t1, F_t2 could be added in figure 3 for more clear understanding and R(t) along with u_i could be more clearly defined (how to obtain u_i is still not clear to me after several re-reading of the paper). The introduced losses should be better justified. Anyway, as I said the paper has some merit, it provides many insights and extensive analysis on “butterfly” method for unsupervised domain adaptation. The experimental section is extensive and demonstrates improvement in performance using this method compared to state-of-the-art, even though for some *real world* datasets (e.g. SUN, Caltech, ImageNet) the improvement is not so significant as in the case of MNIST-SYND. Could the authors provide results on MNIST-SVHN to help compare with other papers in literature? Did the authors observe the same evolution of the accuracy (decreasing and increasing after a few epochs) also on the *real world* datasets as in MNIST-SYND? Replicability: as I said the method is not really clearly explained and therefore I am not confident I could implement and replicate the results. This not because I think the method is complex but because some key components that I pointed out previously about the method are not clear and I strogly believe these are key components to replicate the results.