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Label-Noise Robust Generative Adversarial Networks
[article]
2019
arXiv
pre-print
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
arXiv:1811.11165v2
fatcat:6ybdr2ar2vap5cvr42qjibbkoq