Deep generative models (DGMs) are effective on learning multilayered representations of complex data and performing inference of input data by exploring the generative ability. However, little work has been done on examining whether the representations are discriminative enough to get good prediction performance. In this paper, we present max-margin deep generative models (mmDGMs), which explore the strongly discriminative principle of max-margin learning to improve the discriminative power of DGMs, while retaining the generative capability. We develop an efficient doubly stochastic subgradient algorithm. Empirical results on MNIST and its variant datasets demonstrate that (1) max-margin learning can significantly improve the classification performance of DGMs and meanwhile retain the ability of inferring input data; and (2) mmDGMs are competitive to the state-of-the-art networks that have a similar structure.

本论文提出了最大间隔深度生成模型（mmDGMs），它利用最大间隔学习原理来提高深度生成模型的辨别力，同时保留了生成能力。实验结果表明，最大间隔学习可以显著提高深度生成模型的预测性能，同时保留生成能力。同时，通过使用深度卷积神经网络作为识别和生成模型，mmDGM在MNIST和SVHN数据集上的表现与最先进的完全辨别网络相当竞争力。