In recent years, Generative Adversarial Networks (GANs) have drawn a lot of attentions for learning the underlying distribution of data in various applications. Despite their wide applicability, training GANs is notoriously difficult. This difficulty is due to the min-max nature of the resulting optimization problem and the lack of proper tools of solving general (non-convex, non-concave) min-max optimization problems. In this paper, we try to alleviate this problem by proposing a new generative network that relies on the use of random discriminators instead of adversarial design. This design helps us to avoid the min-max formulation and leads to an optimization problem that is stable and could be solved efficiently. The performance of the proposed method is evaluated using handwritten digits (MNIST) and Fashion products (Fashion-MNIST) data sets. While the resulting images are not as sharp as adversarial training, the use of random discriminator leads to a much faster algorithm as compared to the adversarial counterpart. This observation, at the minimum, illustrates the potential of the random discriminator approach for warm-start in training GANs.
翻译:近年来,创世顶级网络(GANs)吸引了许多注意力来了解各种应用中数据的基本分布。尽管其应用范围很广,但培训GANs却十分困难。这一困难是由于由此产生的优化问题的微量性能,以及缺乏解决一般(非隐形、非隐形)微量最大优化问题的适当工具。在本文中,我们试图通过提出一个新的基因化网络来缓解这一问题,这种网络依靠随机歧视者而不是对抗性设计。这种设计有助于我们避免微轴配方,并导致一个稳定且可以有效解决的最优化问题。拟议方法的性能是通过手写数字(MNIST)和时装产品(Fashon-MNIST)数据集进行评估的。虽然由此产生的图像不如对抗性培训那么敏锐,但随机歧视者的使用导致一种比对抗性对口方更迅速的算法。这一观察至少表明了在GANs培训中随机歧视者方法的起火的可能性。