Most of the deep neural networks require a large amount of data for training to achieve good results. However, in practical anomaly detection applications, we usually only have few labeled anomalous samples that have various types. This study proposed an innovative hybrid architecture that aims to detect anomalies with a small number of labeled anomalous samples. The proposed method consists of two stages. First, the network learns the distribution of normal data with a generative adversarial network (GAN). Second, the discriminator of the network is combined with a classifier and the training process updates different network components depending on whether the labeled samples are normal or anomalies. From the experiments on CIFAR-10 and UC Merced datasets, we demonstrate that our method yields significant performance improvements then another GAN-based approach even when few labeled samples are provided.