论文标题
关于GAN的正标准分类
On Positive-Unlabeled Classification in GAN
论文作者
论文摘要
本文为标准gan定义了一个积极且未标记的分类问题，然后导致一种新型技术来稳定gan中的鉴别剂的训练。传统上，实际数据被视为正面数据，而生成的数据为负。通过鉴别器的学习过程，该积极的分类标准在不考虑生成数据的质量逐渐改善的情况下一直固定在整个过程中，即使它们有时比实际数据更现实。相反，将生成的数据视为未标记的数据更为合理，根据其质量可能是正面的或负面的。因此，歧视者是这个正面和未标记的分类问题的分类器，我们得出了一个新的积极标记的gan（pugan）。从理论上讲，我们讨论了所提出的模型将实现的全球最优性以及等效优化目标。从经验上讲，我们发现普加人可以比那些复杂的歧视稳定方法获得可比甚至更好的性能。
This paper defines a positive and unlabeled classification problem for standard GANs, which then leads to a novel technique to stabilize the training of the discriminator in GANs. Traditionally, real data are taken as positive while generated data are negative. This positive-negative classification criterion was kept fixed all through the learning process of the discriminator without considering the gradually improved quality of generated data, even if they could be more realistic than real data at times. In contrast, it is more reasonable to treat the generated data as unlabeled, which could be positive or negative according to their quality. The discriminator is thus a classifier for this positive and unlabeled classification problem, and we derive a new Positive-Unlabeled GAN (PUGAN). We theoretically discuss the global optimality the proposed model will achieve and the equivalent optimization goal. Empirically, we find that PUGAN can achieve comparable or even better performance than those sophisticated discriminator stabilization methods.
