| Title | Analysis and Research of Generative Adversarial Network in Anomaly Detection |
| Publication Type | Conference Paper |
| Year of Publication | 2022 |
| Authors | Zhang, Lin, Fan, Fuyou, Dai, Yang, He, Chunlin |
| Conference Name | 2022 7th International Conference on Intelligent Computing and Signal Processing (ICSP) |
| Keywords | anomaly detection, Computational modeling, Data models, Deep Learning, Generative Adversarial Learning, generative adversarial network, generative adversarial networks, Metrics, pubcrawl, resilience, Resiliency, Scalability, Signal processing, supervised learning, Time measurement, Training, unsupervised learning |
| Abstract | In recent years, generative adversarial networks (GAN) have become a research hotspot in the field of deep learning. Researchers apply them to the field of anomaly detection and are committed to effectively and accurately identifying abnormal images in practical applications. In anomaly detection, traditional supervised learning algorithms have limitations in training with a large number of known labeled samples. Therefore, the anomaly detection model of unsupervised learning GAN is the research object for discussion and research. Firstly, the basic principles of GAN are introduced. Secondly, several typical GAN-based anomaly detection models are sorted out in detail. Then by comparing the similarities and differences of each derivative model, discuss and summarize their respective advantages, limitations and application scenarios. Finally, the problems and challenges faced by GAN in anomaly detection are discussed, and future research directions are prospected. |
| DOI | 10.1109/ICSP54964.2022.9778761 |
| Citation Key | zhang_analysis_2022 |
Analysis and Research of Generative Adversarial Network in Anomaly Detection