We introduce the Probabilistic Generative Adversarial Network (PGAN), a new GAN variant based on a new kind of objective function. The central idea is to integrate a probabilistic model (a Gaussian Mixture Model, in our case) into the GAN framework which supports a new kind of loss function (based on likelihood rather than classification loss), and at the same time gives a meaningful measure of the quality of the outputs generated by the network. Experiments with MNIST show that the model learns to generate realistic images, and at the same time computes likelihoods that are correlated with the quality of the generated images. We show that PGAN is better able to cope with instability problems that are usually observed in the GAN training procedure. We investigate this from three aspects: the probability landscape of the discriminator, gradients of the generator, and the perfect discriminator problem.

本篇文章介绍了基于概率生成对抗网络 (PGAN) 的新型 GAN 变体， 它将概率模型 (在本例中是高斯混合模型) 整合到GAN框架中，从而支持一种新的损失函数，并提供一种有意义的衡量网络生成输出质量的方法，实验证明 MNIST 数据集的 PGAN 能够生成逼真的图像并计算出与生成图像质量相关的似然度，同时 PGAN 在处理 GAN 训练过程中通常存在的稳定性问题方面具有更好的性能。