GradStop: Exploring Training Dynamics in Unsupervised Outlier Detection through Gradient

TL;DR

GradStop is a novel early stopping algorithm for deep unsupervised outlier detection that leverages training dynamics and gradient cohesion to improve model performance and prevent overfitting.

Contribution

This paper introduces GradStop, a sampling-based, label-free early stopping method that optimizes deep UOD training by estimating real-time performance through gradient analysis.

Findings

GradStop improves deep UOD performance across multiple algorithms and datasets.

Auto Encoder with GradStop outperforms state-of-the-art UOD methods.

Theoretical proofs support the effectiveness of the proposed early stopping approach.

Abstract

Unsupervised Outlier Detection (UOD) is a critical task in data mining and machine learning, aiming to identify instances that significantly deviate from the majority. Without any label, deep UOD methods struggle with the misalignment between the model's direct optimization goal and the final performance goal of Outlier Detection (OD) task. Through the perspective of training dynamics, this paper proposes an early stopping algorithm to optimize the training of deep UOD models, ensuring they perform optimally in OD rather than overfitting the entire contaminated dataset. Inspired by UOD mechanism and inlier priority phenomenon, where intuitively models fit inliers more quickly than outliers, we propose GradStop, a sampling-based label-free algorithm to estimate model's real-time performance during training. First, a sampling method generates two sets: one likely containing more outliers and the other more inliers, then a metric based on gradient cohesion is applied to probe into current training dynamics, which reflects model's performance on OD task. Experimental results on 4 deep UOD algorithms and 47 real-world datasets and theoretical proofs demonstrate the effectiveness of our proposed early stopping algorithm in enhancing the performance of deep UOD models. Auto Encoder (AE) enhanced by GradStop achieves better performance than itself, other SOTA UOD methods, and even ensemble AEs. Our method provides a robust and effective solution to the problem of performance degradation during training, enabling deep UOD models to achieve better potential in anomaly detection tasks.