Anomaly Dataset

Anomaly detection datasets are crucial for developing and evaluating algorithms that identify unusual patterns or events in various data types, ranging from images and videos to sensor readings and tabular data. Current research emphasizes creating realistic and diverse datasets reflecting real-world complexities, including noise, variations in viewpoint, and diverse anomaly types, often focusing on industrial applications and safety-critical systems. These datasets, coupled with the development and benchmarking of novel algorithms like those based on deep learning, normalizing flows, and self-supervised learning, are driving advancements in anomaly detection with significant implications for improving industrial processes, enhancing safety in robotics and autonomous systems, and enabling more effective monitoring in diverse fields.