Anomaly detection is an important task in the computer vision,such as medical,security.One of the challenges in anomaly detection is not easy to obtain large-scale annotated anomalous data.Existing methods focus on one-class classification and weakly supervised learning.Deep support vector data Description(Deep SVDD)is an important method to realize one-class anomaly detection.However,previous Deep SVDD often encounter the hypersphere collapse when constructing the model of the hypersphere.To solve this problem,support vector data description based on spherical regularization(SR-SVDD)is proposed in this paper.SR-SVDD applies the idea of support vectors to optimize the learning process by introducing slack terms.Furthermore,this paper proposes weakly supervised support vector data description based on spherical regularization(SR-WSVDD),which utilizes small amounts of labeled data.Ablation experiments and comparison experiments are carried out on MNIST and CIFAR-10.Experimental results show that,compared with supervised algorithms,the performance of SR-WSVDD is improved by 3.7％on the MNIST,and 16.7％on the CIFAR-10.In addition,compared with other weakly supervised algorithms,SR-WSVDD improves by 1.8％on CIFAR-10 dataset.The proposed SR-SVDD solves the spherical collapse of previous Deep SVDD,and makes the anomaly detection results more accurate.
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