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Soft Curriculum for Learning Conditional GANs with Noisy-Labeled and Uncurated Unlabeled Data

Soft Curriculum for Learning Conditional GANs with Noisy-Labeled and Uncurated Unlabeled Data

Label-noise or curated unlabeled data are used to compensate for the assumption of clean labeled data in training the conditional generative adversarial network; however, satisfying such an extended assumption is occasionally laborious or impractical. As a step towards generative modeling accessible to everyone, we introduce a novel conditional image generation …