  Generative adversarial networks (GANs) are a framework that learns a
generative distribution through adversarial training. Recently, their
class-conditional extensions (e.g., conditional GAN (cGAN) and auxiliary
classifier GAN (AC-GAN)) have attracted much attention owing to their ability
to learn the disentangled representations and to improve the training
stability. However, their training requires the availability of large-scale
accurate class-labeled data, which are often laborious or impractical to
collect in a real-world scenario. To remedy this, we propose a novel family of
GANs called label-noise robust GANs (rGANs), which, by incorporating a noise
transition model, can learn a clean label conditional generative distribution
even when training labels are noisy. In particular, we propose two variants:
rAC-GAN, which is a bridging model between AC-GAN and the label-noise robust
classification model, and rcGAN, which is an extension of cGAN and solves this
problem with no reliance on any classifier. In addition to providing the
theoretical background, we demonstrate the effectiveness of our models through
extensive experiments using diverse GAN configurations, various noise settings,
and multiple evaluation metrics (in which we tested 402 conditions in total).
Our code is available at https://github.com/takuhirok/rGAN/.
