Abstract This paper considers the problem of positive unlabeled (PU) learning. In this context, we propose a two-stage GAN-based model. More specifically, the main contribution is to incorporate a biased PU risk within the standard GAN discriminator loss function. In this manner, the discriminator is constrained to steer the generator to converge towards the unlabeled samples distribution while diverging from the positive samples distribution. Consequently, the proposed model, referred to as D-GAN, exclusively learns the counter-examples distribution without prior knowledge. Experimental results on simple and complex image datasets demonstrate that our approach outperforms state-of-the-art PU methods without prior by overcoming issues such as sensitivity to prior knowledge or first-stage overfitting.

中文翻译：

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从正的未标记图像数据集生成反例

摘要 本文考虑了正未标记（PU）学习的问题。在这种情况下，我们提出了一个基于 GAN 的两阶段模型。更具体地说，主要贡献是在标准 GAN 鉴别器损失函数中加入有偏差的 PU 风险。以这种方式，鉴别器被约束引导生成器向未标记的样本分布收敛，同时从正样本分布发散。因此，所提出的模型，称为 D-GAN，在没有先验知识的情况下专门学习反例分布。在简单和复杂图像数据集上的实验结果表明，我们的方法通过克服对先验知识或第一阶段过度拟合的敏感性等问题，在没有先验的情况下优于最先进的 PU 方法。