Self Supervised Anomaly Detection

Self-Supervised Anomaly Detection is an important subtask in the field of computer vision, aiming to detect abnormal phenomena in images or videos through self-supervised learning methods without the need for labeled data. The goal of this task is to develop a model that can automatically identify deviations from normal patterns, thereby playing a crucial role in scenarios such as industrial inspection, medical diagnosis, and security monitoring, enhancing the robustness and reliability of systems.