Anomaly Detection Datasets

Anomaly detection datasets are crucial for training and evaluating machine learning models capable of identifying unusual patterns or events within data. Current research emphasizes developing diverse and challenging datasets, particularly for real-world applications like industrial inspection and medical imaging, often focusing on high-resolution images and videos with subtle anomalies. Popular model architectures include autoencoders, transformers, and diffusion models, with ongoing efforts to improve their ability to handle multi-class scenarios, open-set problems, and limited labeled data. These advancements are vital for improving the reliability and robustness of anomaly detection systems across various domains.