Outlier Detection Task

Outlier detection aims to identify data points significantly deviating from the norm within a dataset, a crucial task across diverse fields. Current research emphasizes robust methods that handle outlier tasks within multi-task learning frameworks, often employing techniques like robust regularization, energy-based models, and variance-based approaches, sometimes integrated with graph neural networks for structured data. These advancements address limitations in existing methods, particularly concerning data leakage and imbalanced detection performance across different outlier types. Improved outlier detection techniques enhance the reliability and robustness of machine learning models in various applications, from autonomous driving (e.g., LiDAR data processing) to anomaly detection in complex networks.