Discriminative Regularization with Conditional Generative Adversarial Nets for Semi-Supervised Learning

Existing generative adversarial networks (GANs) with manifold regularization for semi-supervised learning (SSL) have shown promising performance in image generation and semi-supervised learning (SSL), which penalize the smoothness of classifier over data manifold based on the smoothness assumption. However, the smoothness assumption is valid for data points in high density region while not hold for data points in low density region, thus they tend to misclassify boundary instances in low density region. In this paper, we propose a novel discriminative regularization method for semi-supervised learning with conditional generative adversarial nets (CGANs). In our method, the discriminative information from class conditional data distribution captured by CGANs is utilized to improve the discrimination of classifier. Different from regular manifold regularization, the discriminative regularization encourages the classifier invariance to local perturbations on the sub-manifold of each cluster, and distinct classification outputs for data points in different clusters. Moreover, our method can be easily implemented via the stochastic approximation without constructing the Laplacian graph or computing the Jacobian of classifier explicitly. Experimental results on benchmark datasets show that our method can achieve competitive performance against previous advanced methods.