Semi-supervised learning has been gaining attention as it allows for performing image analysis tasks such as classification with limited labeled data. Some popular algorithms using Generative Adversarial Networks (GANs) for semi-supervised classification share a single architecture for classification and discrimination. However, this may require a model to converge to a separate data distribution for each task, which may reduce overall performance. While progress in semi-supervised learning has been made, less addressed are small-scale, fully-supervised tasks where even unlabeled data is unavailable and unattainable. We therefore, propose a novel GAN model namely External Classifier GAN (EC-GAN), that utilizes GANs and semi-supervised algorithms to improve classification in fully-supervised regimes. Our method leverages a GAN to generate artificial data used to supplement supervised classification. More specifically, we attach an external classifier, hence the name EC-GAN, to the GAN's generator, as opposed to sharing an architecture with the discriminator. Our experiments demonstrate that EC-GAN's performance is comparable to the shared architecture method, far superior to the standard data augmentation and regularization-based approach, and effective on a small, realistic dataset.

提出了一种名为External Classifier GAN（EC-GAN）的新型GAN模型，利用GAN和半监督算法来改进完全监督区域的分类。通过将外部分类器附加到GAN的生成器上，我们的实验结果表明EC-GAN的表现可与共享架构方法相媲美，远优于标准的数据增强和基于正则化的方法，且可用于小规模、现实数据集。

EC-GAN: 使用半监督算法和 GAN 进行低样本分类