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Robust conditional GANs under missing or uncertain labels
[article]
2019
arXiv
pre-print
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
arXiv:1906.03579v1
fatcat:3qozejre5feztkmzka6wajdrhq