Anomaly Type

Anomaly detection research focuses on identifying data points deviating from established norms, encompassing diverse anomaly types like spikes, discontinuities, and novel features across various data modalities (time series, images, text). Current research emphasizes unsupervised and self-supervised learning approaches, employing architectures such as autoencoders, transformers, and diffusion models, often incorporating techniques like data augmentation and prototype embedding to improve performance across different anomaly types. This field is crucial for enhancing the reliability of machine learning systems in diverse applications, from medical image analysis and industrial process monitoring to cybersecurity and natural language processing, by improving the detection of unexpected or erroneous data.