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Drift-Aware Variational Autoencoder-based Anomaly Detection with Two-level Ensembling

arXiv 2602.12976 ↗First posted 13 Feb 2026cs.LG cs.AIcited by 0

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Abstract (arXiv)

In today's digital world, the generation of vast amounts of streaming data in various domains has become ubiquitous. However, many of these data are unlabeled, making it challenging to identify events, particularly anomalies. This task becomes even more formidable in nonstationary environments where model performance can deteriorate over time due to concept drift. To address these challenges, this paper presents a novel method, VAE++ESDD, which employs incremental learning and two-level ensembling: an ensemble of Variational AutoEncoder(VAEs) for anomaly prediction, along with an ensemble of concept drift detectors. Each drift detector utilizes a statistical-based concept drift mechanism. To evaluate the effectiveness of VAE++ESDD, we conduct a comprehensive experimental study using real-world and synthetic datasets characterized by severely or extremely low anomalous rates and various drift characteristics. Our study reveals that the proposed method significantly outperforms both strong baselines and state-of-the-art methods.

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References · 18

  1. 2016Optimization Methods for Large-Scale Machine Learningcited by 1,140
  2. 2013Stochastic First- and Zeroth-order Methods for Nonconvex Stochastic Programmingcited by 716
  3. 2022A Survey on Unsupervised Anomaly Detection Algorithms for Industrial Imagescited by 21
  4. 2021STUDD: A Student-Teacher Method for Unsupervised Concept Drift Detectioncited by 18
  5. 2021PWPAE: An Ensemble Framework for Concept Drift Adaptation in IoT Data Streamscited by 17
  6. 2020Online Learning With Adaptive Rebalancing in Nonstationary Environmentscited by 16
  7. 2024Unsupervised Concept Drift Detection from Deep Learning Representations in Real-timecited by 16
  8. 2022Adaptive Model Pooling for Online Deep Anomaly Detection from a Complex Evolving Data Streamcited by 13
  9. 2021MemStream: Memory-Based Streaming Anomaly Detectioncited by 12
  10. 2022Nonstationary data stream classification with online active learning and siamese neural networkscited by 12
  11. 2024Dual-Modeling Decouple Distillation for Unsupervised Anomaly Detectioncited by 10
  12. 2023Autoencoder-based Anomaly Detection in Streaming Data with Incremental Learning and Concept Drift Adaptationcited by 9
6 more
  1. 2023Masked Autoencoders for Unsupervised Anomaly Detection in Medical Imagescited by 9
  2. 2023METER: A Dynamic Concept Adaptation Framework for Online Anomaly Detectioncited by 8
  3. 2018Queue-based Resampling for Online Class Imbalance Learningcited by 7
  4. 2024Unsupervised Incremental Learning with Dual Concept Drift Detection for Identifying Anomalous Sequencescited by 5
  5. 2016A Bayesian Ensemble for Unsupervised Anomaly Detectioncited by 1
  6. 2025SiameseDuo++: Active Learning from Data Streams with Dual Augmented Siamese Networkscited by 1

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