Soft Curriculum for Learning Conditional GANs with Noisy-Labeled and
  Uncurated Unlabeled Data
Kai Katsumata Duc Minh Vo Tatsuya Harada Hideki Nakayama
Label-noise or curated unlabeled data is 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
framework that accepts noisy-labeled and uncurated unlabeled data during
training: (i) closed-set and open-set label noise in labeled data and (ii)
closed-set and open-set unlabeled data. To combat it, we propose soft
curriculum learning, which assigns instance-wise weights for adversarial
training while assigning new labels for unlabeled data and correcting wrong
labels for labeled data. Unlike popular curriculum learning, which uses a
threshold to pick the training samples, our soft curriculum controls the effect
of each training instance by using the weights predicted by the auxiliary
classifier, resulting in the preservation of useful samples while ignoring
harmful ones. Our experiments show that our approach outperforms existing
semi-supervised and label-noise robust methods in terms of both quantitative
and qualitative performance. In particular, the proposed approach is able to
match the performance of (semi-) supervised GANs even with less than half the
labeled data.