Wasserstein Adversarial Regularization (WAR) on label noise
Noisy labels often occur in vision datasets, especially when they are obtained from crowdsourcing or Web scraping. We propose a new regularization method, which enables learning robust classifiers in presence of noisy data. To achieve this goal, we propose a new adversarial regularization scheme based on the Wasserstein distance. Using this distance allows taking into account specific relations between classes by leveraging the geometric properties of the labels space. Our Wasserstein Adversarial Regularization (WAR) encodes a selective regularization, which promotes smoothness of the classifier between some classes, while preserving sufficient complexity of the decision boundary between others. We first discuss how and why adversarial regularization can be used in the context of label noise and then show the effectiveness of our method on five datasets corrupted with noisy labels: in both benchmarks and real datasets, WAR outperforms the state-of-the-art competitors.
This paper proposes to use the optimal transport distance to smooth the output of algorithms dealing with label noise. More specifically, the smoothness is on a controlled distance to the input instances, achieved by using the idea of adversarial learning (or robust learning). Generally, the paper is well-organized, with a good writing, and with sufficient experiments. However, I have major concerns about the technical contribution and the experiment parts. 1. It seems that the authors employed the existing adversarial learning techniques directly to the label noise problem. The technique contribution is quite limited. I think the challenging and interesting part should be about how to learn the metric C. Currently, the authors use the semantic distances between different class labels to set-up C. This intuitively makes sense but not convincing. We expect the authors to put more effort to study C, which may depend on the label noise rates or the geometric information of the instances. An in-depth study of C will make the technical contribution solid. 2. The experiment part looks sufficient but may be problematic. For example, in the subsection about Clothing1M, the authors directly cited the results from others while the experimental setting could be very different. It is not convincing to make the conclusion that the proposed method has the best performance.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Wasserstein Adversarial Regularization (WAR) on label noise
Noisy labels often occur in vision datasets, especially when they are obtained from crowdsourcing or Web scraping. We propose a new regularization method, which enables learning robust classifiers in presence of noisy data. To achieve this goal, we propose a new adversarial regularization scheme based on the Wasserstein distance. Using this distance allows taking into account specific relations between classes by leveraging the geometric properties of the labels space. Our Wasserstein Adversarial Regularization (WAR) encodes a selective regularization, which promotes smoothness of the classifier between some classes, while preserving sufficient complexity of the decision boundary between others. We first discuss how and why adversarial regularization can be used in the context of label noise and then show the effectiveness of our method on five datasets corrupted with noisy labels: in both benchmarks and real datasets, WAR outperforms the state-of-the-art competitors.
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 proposes to use the optimal transport distance to smooth the output of algorithms dealing with label noise. More specifically, the smoothness is on a controlled distance to the input instances, achieved by using the idea of adversarial learning (or robust learning). Generally, the paper is well-organized, with a good writing, and with sufficient experiments. However, I have major concerns about the technical contribution and the experiment parts. 1. It seems that the authors employed the existing adversarial learning techniques directly to the label noise problem. The technique contribution is quite limited. I think the challenging and interesting part should be about how to learn the metric C. Currently, the authors use the semantic distances between different class labels to set-up C. This intuitively makes sense but not convincing. We expect the authors to put more effort to study C, which may depend on the label noise rates or the geometric information of the instances. An in-depth study of C will make the technical contribution solid. 2. The experiment part looks sufficient but may be problematic. For example, in the subsection about Clothing1M, the authors directly cited the results from others while the experimental setting could be very different. It is not convincing to make the conclusion that the proposed method has the best performance.
assistant
This paper proposes to use the optimal transport distance to smooth the output of algorithms dealing with label noise. More specifically, the smoothness is on a controlled distance to the input instances, achieved by using the idea of adversarial learning (or robust learning). Generally, the paper is well-organized, with a good writing, and with sufficient experiments. However, I have major concerns about the technical contribution and the experiment parts. 1. It seems that the authors employed the existing adversarial learning techniques directly to the label noise problem. The technique contribution is quite limited. I think the challenging and interesting part should be about how to learn the metric C. Currently, the authors use the semantic distances between different class labels to set-up C. This intuitively makes sense but not convincing. We expect the authors to put more effort to study C, which may depend on the label noise rates or the geometric information of the instances. An in-depth study of C will make the technical contribution solid. 2. The experiment part looks sufficient but may be problematic. For example, in the subsection about Clothing1M, the authors directly cited the results from others while the experimental setting could be very different. It is not convincing to make the conclusion that the proposed method has the best performance.