  Matching the performance of conditional Generative Adversarial Networks with
little supervision is an important task, especially in venturing into new
domains. We design a new training algorithm, which is robust to missing or
ambiguous labels. The main idea is to intentionally corrupt the labels of
generated examples to match the statistics of the real data, and have a
discriminator process the real and generated examples with corrupted labels. We
showcase the robustness of this proposed approach both theoretically and
empirically. We show that minimizing the proposed loss is equivalent to
minimizing true divergence between real and generated data up to a
multiplicative factor, and characterize this multiplicative factor as a
function of the statistics of the uncertain labels. Experiments on MNIST
dataset demonstrates that proposed architecture is able to achieve high
accuracy in generating examples faithful to the class even with only a few
examples per class.
