Anomaly Detection Benchmark

Anomaly detection benchmarks are crucial for evaluating and comparing algorithms that identify unusual patterns in diverse data types, ranging from images and videos to time series and tabular data. Current research focuses on developing comprehensive benchmarks with varied anomaly types and realistic data characteristics, including synthetic datasets to address data scarcity and privacy concerns, and exploring various model architectures such as autoencoders, transformers, and methods leveraging large language models. These benchmarks are vital for advancing the field by facilitating rigorous algorithm evaluation, promoting fair comparisons, and ultimately improving the accuracy and robustness of anomaly detection in various real-world applications, such as industrial quality control, cybersecurity, and healthcare.