# METER: A Dynamic Concept Adaptation Framework for Online Anomaly Detection

arXiv: https://arxiv.org/abs/2312.16831 · HTML: https://mlsanity.com/paper/2312.16831/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 90 references that are in the map.
- First posted on arXiv 28 Dec 2023.

## Builds on (most cited first, 22 of 22)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms](https://mlsanity.com/paper/1708.07747/) (2017)
- [HyperNetworks](https://mlsanity.com/paper/1609.09106/) (2016)
- [Evidential Deep Learning to Quantify Classification Uncertainty](https://mlsanity.com/paper/1806.01768/) (2018)
- [Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection](https://mlsanity.com/paper/1904.02639/) (2019)
- [Learning under Concept Drift: A Review](https://mlsanity.com/paper/2004.05785/) (2020)
- [A Unifying Review of Deep and Shallow Anomaly Detection](https://mlsanity.com/paper/2009.11732/) (2020)
- [PyOD: A Python Toolbox for Scalable Outlier Detection](https://mlsanity.com/paper/1901.01588/) (2019)
- [Wider or Deeper: Revisiting the ResNet Model for Visual Recognition](https://mlsanity.com/paper/1611.10080/) (2016)
- [Trusted Multi-View Classification with Dynamic Evidential Fusion](https://mlsanity.com/paper/2204.11423/) (2022)
- [Kitsune: An Ensemble of Autoencoders for Online Network Intrusion Detection](https://mlsanity.com/paper/1802.09089/) (2018)
- [Adversarially Learned Anomaly Detection](https://mlsanity.com/paper/1812.02288/) (2018)
- [Challenges in Benchmarking Stream Learning Algorithms with Real-world Data](https://mlsanity.com/paper/2005.00113/) (2020)
- [Robust Subspace Recovery Layer for Unsupervised Anomaly Detection](https://mlsanity.com/paper/1904.00152/) (2019)
- [ARM-Net: Adaptive Relation Modeling Network for Structured Data](https://mlsanity.com/paper/2107.01830/) (2021)
- [Anomaly detection in online social networks](https://mlsanity.com/paper/1608.00301/) (2016)
- [PIDForest: Anomaly Detection via Partial Identification](https://mlsanity.com/paper/1912.03582/) (2019)
- [TRACER: A Framework for Facilitating Accurate and Interpretable Analytics for High Stakes Applications](https://mlsanity.com/paper/2003.12012/) (2020)
- [Adaptive Performance Anomaly Detection for Online Service Systems via Pattern Sketching](https://mlsanity.com/paper/2201.02944/) (2022)
- [Adaptive Model Pooling for Online Deep Anomaly Detection from a Complex Evolving Data Stream](https://mlsanity.com/paper/2206.04792/) (2022)
- [PySAD: A Streaming Anomaly Detection Framework in Python](https://mlsanity.com/paper/2009.02572/) (2020)

## Built on by (most cited first, 9 of 9)

- [Do LLMs Understand Visual Anomalies? Uncovering LLM's Capabilities in Zero-shot Anomaly Detection](https://mlsanity.com/paper/2404.09654/) (2024)
- [Generative AI for Healthcare: Fundamentals, Challenges, and Perspectives](https://mlsanity.com/paper/2510.24551/) (2025)
- [pTNAS: Progressive Neural Architecture Search for Tabular Data](https://mlsanity.com/paper/2403.10318/) (2024)
- [CANDI: Curated Test-Time Adaptation for Multivariate Time-Series Anomaly Detection Under Distribution Shift](https://mlsanity.com/paper/2604.01845/) (2026)
- [Improving Real-Time Concept Drift Detection using a Hybrid Transformer-Autoencoder Framework](https://mlsanity.com/paper/2508.07085/) (2025)
- [Drift-Aware Variational Autoencoder-based Anomaly Detection with Two-level Ensembling](https://mlsanity.com/paper/2602.12976/) (2026)
- [Catching Every Ripple: Enhanced Anomaly Awareness via Dynamic Concept Adaptation](https://mlsanity.com/paper/2604.14726/) (2026)
- [TPA-AD: A Two-Stage Pseudo Anomaly-Guided Method for Bearing Time-Series Anomaly Detection](https://mlsanity.com/paper/2606.04073/) (2026)
- [SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift](https://mlsanity.com/paper/2607.18072/) (2026)

## Abstract (arXiv)

Real-time analytics and decision-making require online anomaly detection (OAD) to handle drifts in data streams efficiently and effectively. Unfortunately, existing approaches are often constrained by their limited detection capacity and slow adaptation to evolving data streams, inhibiting their efficacy and efficiency in handling concept drift, which is a major challenge in evolving data streams. In this paper, we introduce METER, a novel dynamic concept adaptation framework that introduces a new paradigm for OAD. METER addresses concept drift by first training a base detection model on historical data to capture recurring central concepts, and then learning to dynamically adapt to new concepts in data streams upon detecting concept drift. Particularly, METER employs a novel dynamic concept adaptation technique that leverages a hypernetwork to dynamically generate the parameter shift of the base detection model, providing a more effective and efficient solution than conventional retraining or fine-tuning approaches. Further, METER incorporates a lightweight drift detection controller, underpinned by evidential deep learning, to support robust and interpretable concept drift detection. We conduct an extensive experimental evaluation, and the results show that METER significantly outperforms existing OAD approaches in various application scenarios.