论文标题
EC-GAN：使用半监督算法和GAN的低样本分类
EC-GAN: Low-Sample Classification using Semi-Supervised Algorithms and GANs
论文作者
论文摘要
半监督学习一直在引起人们的注意，因为它允许执行图像分析任务，例如具有有限标记数据的分类。使用生成对抗网络（GAN）进行半监督分类的一些流行算法共享单个体系结构，以进行分类和歧视。但是，这可能需要模型将每个任务的单独数据分配收敛，这可能会降低整体性能。尽管已经取得了半监督学习的进展，但较少的解决方案是小规模，完全监督的任务，即使没有标记的数据也无法获得和无法实现。因此，我们提出了一种新型的GAN模型，即外部分类器GAN（EC-GAN），该模型利用gans和半监督算法来改善完全监督的制度中的分类。我们的方法利用gan生成用于补充监督分类的人工数据。更具体地说，我们将外部分类器附加到GAN的发电机上，而不是与歧视者共享架构。我们的实验表明，EC-GAN的性能与共享体系结构方法相当，远优于标准数据增强和基于正则化的方法，并且在小型，现实的数据集中有效。
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.
