| Title | Improving Anomaly Detection with a Self-Supervised Task Based on Generative Adversarial Network |
| Publication Type | Conference Paper |
| Year of Publication | 2022 |
| Authors | Chai, Heyan, Su, Weijun, Tang, Siyu, Ding, Ye, Fang, Binxing, Liao, Qing |
| Conference Name | ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) |
| Keywords | annotations, anomaly detection, Conferences, Data models, Diversity methods, Generative Adversarial Learning, generative adversarial networks, interpolation, Metrics, pubcrawl, resilience, Resiliency, Scalability, self-supervision, Signal processing, Task Analysis |
| Abstract | 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. |
| Notes | ISSN: 2379-190X |
| DOI | 10.1109/ICASSP43922.2022.9747461 |
| Citation Key | chai_improving_2022 |
Improving Anomaly Detection with a Self-Supervised Task Based on Generative Adversarial Network