In this paper we present a method for learning a discriminative classifier from unlabeled or partially labeled data. Our approach is based on an objective function that trades-off mutual information between observed examples and their predicted categorical class distribution, against robustness of the classifier to an adversarial generative model. The resulting algorithm can either be interpreted as a natural generalization of the generative adversarial networks (GAN) framework or as an extension of the regularized information maximization (RIM) framework to robust classification against an optimal adversary. We empirically evaluate our method - which we dub categorical generative adversarial networks (or CatGAN) - on synthetic data as well as on challenging image classification tasks, demonstrating the robustness of the learned classifiers. We further qualitatively assess the fidelity of samples generated by the adversarial generator that is learned alongside the discriminative classifier, and identify links between the CatGAN objective and discriminative clustering algorithms (such as RIM).

本文提出了一种从未标记或部分标记的数据中学习判别分类器的方法，该方法基于一种目标函数，该函数在观察到的示例和它们预测的分类分布之间进行互信息交换，并对分类器抵抗对抗性生成模型的鲁棒性进行了权衡。我们实证评估了我们的方法-我们将其命名为分类生成对抗网络（或CatGAN）-在合成数据以及具有挑战性的图像分类任务上，展示了所学习分类器的鲁棒性。此外，我们定性地评估了由随着判别器学习的对抗性生成器生成的样本的保真度，并确定了CatGAN目标和判别聚类算法（如RIM）之间的联系。

基于生成对抗网络的无监督与半监督分类学习