We study the problem of learning generative adversarial networks (GANs) for a rare class of an unlabeled dataset subject to a labeling budget. This problem is motivated from practical applications in domains including security (e.g., synthesizing packets for DNS amplification attacks), systems and networking (e.g., synthesizing workloads that trigger high resource usage), and machine learning (e.g., generating images from a rare class). Existing approaches are unsuitable, either requiring fully-labeled datasets or sacrificing the fidelity of the rare class for that of the common classes. We propose RareGAN, a novel synthesis of three key ideas: (1) extending conditional GANs to use labelled and unlabelled data for better generalization; (2) an active learning approach that requests the most useful labels; and (3) a weighted loss function to favor learning the rare class. We show that RareGAN achieves a better fidelity-diversity tradeoff on the rare class than prior work across different applications, budgets, rare class fractions, GAN losses, and architectures.

本研究提出了RareGAN，一种新的合成方法，通过使用带标签和未标记数据进行更好的泛化，请求最有用的标签，以及加权损失函数来倾向于学习罕见类，解决了针对标签预算下罕见类非完全标记的数据集学习生成式对抗网络的问题，并在不同应用中实现了比以前更好的保真度和多样性权衡。

RareGAN: 生成罕见类别的样本