Below are the abstract, introduction, and conclusion of a computer science research paper. Please summarize the main contribution of the work in a single sentence. Your response should include the summary and no additional text.

Paper text:

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.
Deep neural networks (DNNs) trained by large-scale datasets have enabled many breakthroughs in machine learning, especially in various classification tasks such as image classification (He et al., 2016a) , object detection (Redmon & Farhadi, 2017) , and speech recognition (Park et al., 2019) .
Here, a practical issue in this large-scale training regime, however, is at the difficulty in data acquisition process across labels, e.g. some labels are more abundant and easier to collect (Mahajan et al., 2018) .
This often leads a dataset to have "long-tailed" label distribution, as frequently found in modern real-world large-scale datasets.
Such class-imbalanced datasets make the standard training of DNN harder to generalize (Wang et al., 2017; Ren et al., 2018; Dong et al., 2018) , particularly if one requires a class-balanced performance metric for a practical reason.
A natural approach in attempt to bypass this class-imbalance problem is to re-balance the training objective artificially in class-wise with respect to their numbers of samples.
Two of such methods are representative:
(a) "re-weighting" the given loss function by a factor inversely proportional to the sample frequency in class-wise (Huang et al., 2016; Khan et al., 2017) , and
(b) "re-sampling" the given dataset so that the expected sampling distribution during training can be balanced, either by "over-sampling" the minority classes (Japkowicz, 2000; Cui et al., 2018) or "under-sampling" the majority classes (He & Garcia, 2008) .
The methods on this line, however, usually result in harsh over-fitting to minority classes, since in essence, they cannot handle the lack of information on minority data.
Several attempts have been made to alleviate this over-fitting issue: Cui et al. (2019) proposed the concept of "effective number" of samples as alternative weights in the re-weighting method.
In the context of re-sampling, on the other hand, SMOTE (Chawla et al., 2002 ) is a widely-used variant of the over-sampling method that mitigates the over-fitting via data augmentation, but generally this direction has not been much explored recently.
Cao et al. (2019) found that both re-weighting and re-sampling can be much more effective when applied at the later stage of training, in case of neural networks.
Another line of the research attempts to prevent the over-fitting with a new regularization scheme that minority classes are more regularized, where the margin-based approaches generally suit well as a form of data-dependent regularizer (Zhang et al., 2017; Dong et al., 2018; Khan et al., 2019; Cao et al., 2019) .
There have also been works that view the class-imbalance problem in the framework of active learning (Ertekin et al., 2007; Attenberg & Ertekin, 2013) or meta-learning (Wang et al., 2017; Ren et al., 2018; Shu et al., 2019; Liu et al., 2019) .
Contribution.
In this paper, we revisit the over-sampling framework and propose a new way of generating minority samples, coined Adversarial Minority Over-sampling (AMO).
In contrast to other over-sampling methods, e.g. SMOTE (Chawla et al., 2002) that applies data augmentation to minority samples to mitigate the over-fitting issue, we attempt to generate minority samples in a completely different way: AMO does not use the existing minority samples for synthesis, but use adversarial examples (Szegedy et al., 2014; Goodfellow et al., 2015) of non-minority samples made from another, baseline classifier (potentially, over-fitted to minority classes) independently trained using the given imbalanced dataset.
This motivation leads us to a very counter-intuitive method at a first glance: it results in labeling minority class on an adversarial example of a majority class at last.
Our key finding is that, this method actually can be very effective on learning generalizable features in the imbalanced learning: it does not overly use the minority samples, and leverages the richer information of the majority samples simultaneously.
Our minority over-sampling method consists of three components to improve the sampling quality.
First, we propose an optimization objective for generating synthetic samples, so that a majority input can be translated into a synthetic minority sample via optimizing it, while not affecting the performance of the majority class (even the sample is labeled to the minority class).
Second, we design a sample rejection criteria based on the observation that generation from more majority class is more preferable.
Third, based on the proposed rejection criteria, we suggest an optimal distribution for sampling the initial seed points of the generation.
We evaluate our method on various imbalanced classification problems, including synthetically imbalanced CIFAR-10/100 (Krizhevsky, 2009) , and real-world imbalanced datasets including Twitter dataset (Gimpel et al., 2011) and Reuters dataset (Lewis et al., 2004) in natural language processing.
Despite its simplicity, our method of adversarial minority over-sampling significantly improves the balanced test accuracy compared to previous re-sampling or re-weighting methods across all the tested datasets.
These results even surpass the results from state-of-the-art margin-based method (LDAM; Cao et al. 2019) .
We also highlight that our method is fairly orthogonal to the regularization-based methods, by showing that joint training of our method with LDAM could further improve the balanced test accuracy as well.
Despite the great generalization ability of DNNs, they are known to be susceptible to adversarial examples, which makes it difficult to deploy them in real-world safety-critical applications (Szegedy et al., 2014; Goodfellow et al., 2015) .
The broad existence of adversarial examples in DNNs is still a mysterious phenomenon (Gilmer et al., 2019; Galloway et al., 2019; Ilyas et al., 2019) , and we think our results can be of independent interest to shed new insight on understanding their property.
We propose a new over-sampling method for imbalanced classification, called Advserarial Minority Over-sampling (AMO).
The problems we explored in this paper lead us to an essential question that whether an adversarial perturbation could be a good feature.
Our findings suggest that it could be at least to improve imbalanced learning, where the minority classes suffer over-fitting due to insufficient data.
We believe our method could open a new direction of research both in imbalanced learning and adversarial examples.
We develop a new method for imbalanced classification using adversarial examples