Generative Adversarial Network (GAN)-based models have made remarkable progress in high-fidelity image synthesis. However, the performance of class-conditional image synthesis may significantly deteriorate for the case where a limited number of labeled training samples are available. To reduce the dependence on labeled data, we propose a semi-supervised GAN with Extended Distribution Matching, and our model is referred to as EDM-GAN. To prevent a class-conditional discriminator from overfitting the limited labeled data, we perform a transformation of random regional replacement on both real and synthesized samples. By matching the extended distributions, the discriminator is encouraged to focus more on the spatial regions that contain certain objects, while at the same time a class-conditional generator is induced to capture precise class semantics. The adversarial training process can be effectively stabilized and converges to a better solution. Our experimental results on multiple standard benchmarks demonstrate consistent performance gains in synthesis quality and class-semantic accuracy.

Semi-supervised generative learning aims to learn the underlying class-conditional distribution of partially labeled data. Generative Adversarial Networks (GANs) have led to promising progress in this task. However, it still needs to further explore the issue of imbalance between real labeled data and fake data in the adversarial learning process. To address this issue, we propose a regularization technique based on Random Regional Replacement (R3-regularization) to facilitate the generative learning process. Specifically, we construct two types of between-class instances: cross-category ones and real-fake ones. These instances could be closer to the decision boundaries and are important for regularizing the classification and discriminative networks in our class-conditional GANs, which we refer to as R3-CGAN. Better guidance from these two networks makes the generative network produce instances with class-specific information and high fidelity. We experiment with multiple standard benchmarks, and demonstrate that the R3-regularization can lead to significant improvement in both classification and class-conditional image synthesis.

Learning class-conditional data distributions is crucial for Generative Adversarial Networks (GAN) in semi-supervised learning. To improve both instance synthesis and classification in this setting, we propose an enhanced TripleGAN (EnhancedTGAN) model in this work. We follow the adversarial training scheme of the original TripleGAN, but completely re-design the training targets of the generator and classifier. Specifically, we adopt feature-semantics matching to enhance the generator in learning class-conditional distributions from both the aspects of statistics in the latent space and semantics consistency with respect to the generator and classifier. Since a limited amount of labeled data is not sufficient to determine satisfactory decision boundaries, we include two classifiers, and incorporate collaborative learning into our model to provide better guidance for generator training. The synthesized high-fidelity data can in turn be used for improving classifier training. In the experiments, the superior performance of our approach on multiple benchmark datasets demonstrates the effectiveness of the mutual reinforcement between the generator and classifiers in facilitating semi-supervised instance synthesis and classification.