Biblio

Existing anomaly detection models show success in detecting abnormal images with generative adversarial networks on the insufficient annotation of anomalous samples. However, existing models cannot accurately identify the anomaly samples which are close to the normal samples. We assume that the main reason is that these methods ignore the diversity of patterns in normal samples. To alleviate the above issue, this paper proposes a novel anomaly detection framework based on generative adversarial network, called ADe-GAN. More concretely, we construct a self-supervised learning task to fully explore the pattern information and latent representations of input images. In model inferring stage, we design a new abnormality score approach by jointly considering the pattern information and reconstruction errors to improve the performance of anomaly detection. Extensive experiments show that the ADe-GAN outperforms the state-of-the-art methods over several real-world datasets.

This article analyzes the basic concepts of supply chain finance, participating institutions, business methods, and exposure to risks. The author combined the basic content of the Internet of Things and block chain technology to carry out research. This paper studies the specific applications of the Internet of Things and block chain technology in supply chain financial risk identification, supply chain financial risk assessment, full-process logistics supervision, smart contract transaction management, corporate financial statement sorting, and risk prevention measures. The author's purpose is to improve the financial risk management level of the enterprise supply chain and promote the stable development of the enterprise economy.