Anomaly Aware

Anomaly-aware methods focus on identifying deviations from normal patterns in data, a crucial task across diverse fields like medical imaging and industrial quality control. Current research emphasizes robust model architectures, including autoencoders (particularly patch-wise variations) and diffusion models, to address challenges like limited training data and the inherent complexity of anomalies. These advancements are improving the accuracy and interpretability of anomaly detection, leading to more reliable systems for applications ranging from medical diagnosis to predictive maintenance. The development of cross-domain techniques further enhances the applicability of these methods by leveraging information from related datasets.