迭代化代价函数及超参数可变的生成对抗网络

Iterative cost function and variable parameter generative adversarial networks

为了解决生成对抗网络(Generative adversarial networks,GAN)的训练难问题,该文在Wasserstein GAN(WGAN)方法基础上提出了迭代化代价函数及超参数可变的生成对抗网络。为了对原始WGAN中的惩罚项进行改进,用迭代的方法增加惩罚项代替原始随机选取的方法。针对WGAN中固定代价函数惩罚项的超参数,提出变动超参数策略,其变动的依据是仿分布和真实分布之间的距离。在MNIST手写字体数据集和CELEBA人脸数据集上的实验表明,与传统WGAN方法相比,该文方法在生成器的拟合速度上有了显著提高,充分验证了方法的有效性。

In order to solve the difficult training problem of generative adversarial networks,this paper proposes an iterative cost function and variable parameter generative adversarial networks based on the Wasserstein GAN(WGAN)method. For the improvement of penalty items in the original WGAN,iterative methods are used to increase penalty instead of the original randomly selected method. Aiming at the hyper-parameter of penalty item of fixed cost function in WGAN,the strategy of changing hyper-parameter is put forward. The change is based on the distance between imitation distribution and real distribution. Experiments conducted on MNIST handwritten font datasets and CELEBA face datasets show the effectiveness of the proposed method as compared with the traditional WGAN,significantly improving the convergence speed of the generator.