Unsupervised anomaly detection based on improved skip-gannomaly

The current deep learning detection algorithms generally require a large amount of labeled data and it is difficult to collect samples in some application scenarios. An anomaly detection algorithm based on improved Skip-GANomaly is proposed. The algorithm firstly enhances the network's ability to extract image space and channel information by adding an attention mechanism module, and improves the network's ability to extract features. Then, on this basis, this paper uses mixed depth wise convolutional to replace ordinary convolution, so that the network can reduce the number of parameters while enhancing the network's ability to capture different types of patterns from the input image. The experimental results show that the AUC of the algorithm in different categories on the CIFAR10 dataset is generally higher than Skip-GANomaly and its anomaly detection model.