  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.
