Deep Transductive Outlier Detection
This addresses outlier detection for machine learning practitioners, offering a novel transductive approach with significant performance gains, though it is incremental in applying transductive learning to this specific task.
The paper tackled outlier detection by introducing Doust, the first end-to-end transductive deep learning algorithm that leverages unlabeled test data, achieving an average ROC-AUC of 89% on the ADBench benchmark and outperforming 21 competitors by about 10%.
Outlier detection (OD) is one of the core challenges in machine learning. Transductive learning, which leverages test data during training, has shown promise in related machine learning tasks, yet remains largely unexplored for modern OD. We present Doust, the first end-to-end transductive deep learning algorithm for outlier detection, which explicitly leverages unlabeled test data to boost accuracy. On the comprehensive ADBench benchmark, Doust achieves an average ROC-AUC of $89%$, outperforming all 21 competitors by roughly $10%$. Our analysis identifies both the potential and a limitation of transductive OD: while performance gains can be substantial in favorable conditions, very low contamination rates can hinder improvements unless the dataset is sufficiently large.