Imbalanced Classification via Adversarial Minority Over-sampling
In most real-world scenarios, training datasets are highly class-imbalanced, where deep neural networks suffer from generalizing to a balanced testing criterion. In this paper, we explore a novel yet simple way to alleviate this issue via synthesizing less-frequent classes with adversarial examples of other classes. Surprisingly, we found this counter-intuitive method can effectively learn generalizable features of minority classes by transferring and leveraging the diversity of the majority information. Our experimental results on various types of class-imbalanced datasets in image classification and natural language processing show that the proposed method not only improves the generalization of minority classes significantly compared to other re-sampling or re-weighting methods, but also surpasses other methods of state-of-art level for the class-imbalanced classification.
The authors propose to tackle imbalance classification using re-sampling methods. The idea is to generate adversarial examples of a model trained on the imbalanced dataset, in the least frequent classes. To the best of my understanding the rational behind this is that models usually overfit the least frequent class. Hence adversarial examples in the minority class would help to train a new model that generalize better. Q1. If a model is trained to perform well on the class-imbalanced dataset and is robust to adversarial attacks / does not overfit the minority classes, what would happen? My decision is weak reject for the following reasons. While the idea seems appealing, intuitive ideas on why such a methodology should work. Moreover rather than trying to give more understanding on why such a methodology works, the experiments only illustrate that the propose algorithm outperform the state of the art. While such positive results are always welcome, it is also useful to propose experiments to gain a better understanding on the performances/limits of an algorithm. Also, the proposed algorithm depends on some hyper-parameters that are never empirically nor theoretically studied. Suggestions ----------------- while it is nice to report standard deviations, the authors could increase the number of runs to make the differences more significant and eventually provide a statistical test.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Imbalanced Classification via Adversarial Minority Over-sampling
In most real-world scenarios, training datasets are highly class-imbalanced, where deep neural networks suffer from generalizing to a balanced testing criterion. In this paper, we explore a novel yet simple way to alleviate this issue via synthesizing less-frequent classes with adversarial examples of other classes. Surprisingly, we found this counter-intuitive method can effectively learn generalizable features of minority classes by transferring and leveraging the diversity of the majority information. Our experimental results on various types of class-imbalanced datasets in image classification and natural language processing show that the proposed method not only improves the generalization of minority classes significantly compared to other re-sampling or re-weighting methods, but also surpasses other methods of state-of-art level for the class-imbalanced classification.
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 to tackle imbalance classification using re-sampling methods. The idea is to generate adversarial examples of a model trained on the imbalanced dataset, in the least frequent classes. To the best of my understanding the rational behind this is that models usually overfit the least frequent class. Hence adversarial examples in the minority class would help to train a new model that generalize better. Q1. If a model is trained to perform well on the class-imbalanced dataset and is robust to adversarial attacks / does not overfit the minority classes, what would happen? My decision is weak reject for the following reasons. While the idea seems appealing, intuitive ideas on why such a methodology should work. Moreover rather than trying to give more understanding on why such a methodology works, the experiments only illustrate that the propose algorithm outperform the state of the art. While such positive results are always welcome, it is also useful to propose experiments to gain a better understanding on the performances/limits of an algorithm. Also, the proposed algorithm depends on some hyper-parameters that are never empirically nor theoretically studied. Suggestions ----------------- while it is nice to report standard deviations, the authors could increase the number of runs to make the differences more significant and eventually provide a statistical test.
assistant
The authors propose to tackle imbalance classification using re-sampling methods. The idea is to generate adversarial examples of a model trained on the imbalanced dataset, in the least frequent classes. To the best of my understanding the rational behind this is that models usually overfit the least frequent class. Hence adversarial examples in the minority class would help to train a new model that generalize better. Q1. If a model is trained to perform well on the class-imbalanced dataset and is robust to adversarial attacks / does not overfit the minority classes, what would happen? My decision is weak reject for the following reasons. While the idea seems appealing, intuitive ideas on why such a methodology should work. Moreover rather than trying to give more understanding on why such a methodology works, the experiments only illustrate that the propose algorithm outperform the state of the art. While such positive results are always welcome, it is also useful to propose experiments to gain a better understanding on the performances/limits of an algorithm. Also, the proposed algorithm depends on some hyper-parameters that are never empirically nor theoretically studied. Suggestions ----------------- while it is nice to report standard deviations, the authors could increase the number of runs to make the differences more significant and eventually provide a statistical test.