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cs.LG2017★ 4 cited
Generative Adversarial Positive-Unlabelled Learning
Ming Hou, Brahim Chaib-draa, Chao Li +1
In this work, we consider the task of classifying binary positive-unlabeled (PU) data. The existing discriminative learning based PU models attempt to seek an optimal reweighting s…
cs.LG2017★ 6 cited
Tensorizing Generative Adversarial Nets
Xingwei Cao, Xuyang Zhao, Qibin Zhao
Generative Adversarial Network (GAN) and its variants exhibit state-of-the-art performance in the class of generative models. To capture higher-dimensional distributions, the commo…