# STUDD: A Student-Teacher Method for Unsupervised Concept Drift Detection

arXiv: https://arxiv.org/abs/2103.00903 · HTML: https://mlsanity.com/paper/2103.00903/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 47 references that are in the map.
- First posted on arXiv 1 Mar 2021.

## Builds on (most cited first, 7 of 7)

- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Time for a change: a tutorial for comparing multiple classifiers through Bayesian analysis](https://mlsanity.com/paper/1606.04316/) (2016)
- [Scikit-Multiflow: A Multi-output Streaming Framework](https://mlsanity.com/paper/1807.04662/) (2018)
- [Challenges in Benchmarking Stream Learning Algorithms with Real-world Data](https://mlsanity.com/paper/2005.00113/) (2020)
- [Request-and-Reverify: Hierarchical Hypothesis Testing for Concept Drift Detection with Expensive Labels](https://mlsanity.com/paper/1806.10131/) (2018)
- [Automatic Model Monitoring for Data Streams](https://mlsanity.com/paper/1908.04240/) (2019)

## Built on by (most cited first, 19 of 19)

- [Online Drift Detection with Maximum Concept Discrepancy](https://mlsanity.com/paper/2407.05375/) (2024)
- [A comprehensive analysis of concept drift locality in data streams](https://mlsanity.com/paper/2311.06396/) (2023)
- [When to retrain a machine learning model](https://mlsanity.com/paper/2505.14903/) (2025)
- [Resilient Contrastive Pre-training under Non-Stationary Drift](https://mlsanity.com/paper/2502.07620/) (2025)
- [Walking the Tightrope: Disentangling Beneficial and Detrimental Drifts in Non-Stationary Custom-Tuning](https://mlsanity.com/paper/2505.13081/) (2025)
- [Unsupervised Detection of Behavioural Drifts with Dynamic Clustering and Trajectory Analysis](https://mlsanity.com/paper/2302.06228/) (2023)
- [IncA-DES: An incremental and adaptive dynamic ensemble selection approach using online K-d tree neighborhood search for data streams with concept drift](https://mlsanity.com/paper/2507.12573/) (2025)
- [Towards Computational Performance Engineering for Unsupervised Concept Drift Detection -- Complexities, Benchmarking, Performance Analysis](https://mlsanity.com/paper/2304.08319/) (2023)
- [The Window Dilemma: Why Concept Drift Detection is Ill-Posed](https://mlsanity.com/paper/2602.06456/) (2026)
- [A Framework for Evaluating and Benchmarking Concept Drift Detection Methods](https://mlsanity.com/paper/2606.07789/) (2026)
- [Online Feature Selection for Efficient Learning in Networked Systems](https://mlsanity.com/paper/2112.08253/) (2021)
- [Time to Retrain? Detecting Concept Drifts in Machine Learning Systems](https://mlsanity.com/paper/2410.09190/) (2024)
- [SUDS: A Strategy for Unsupervised Drift Sampling](https://mlsanity.com/paper/2411.02995/) (2024)
- [Drift-Aware Variational Autoencoder-based Anomaly Detection with Two-level Ensembling](https://mlsanity.com/paper/2602.12976/) (2026)
- [DT-GOL: Dual-Track Geometric Online Learning in Nonstationary Environment with Label Delay](https://mlsanity.com/paper/2606.22950/) (2026)
- [Dimension-Calibrated Unexplained Mass: An Interpretable Drift Statistic for Contamination Monitoring in Data Streams](https://mlsanity.com/paper/2607.16811/) (2026)
- [Maintaining IoT Device Identification under Concept Drift via Budget-Aware Traffic Labeling](https://mlsanity.com/paper/2608.15465/) (2026)
- [Performance Drift Detection in Machine Learning as a Service (MLaaS) for IoT Environments](https://mlsanity.com/paper/2608.18555/) (2026)
- [Does the Explanation Drift Before the Guarantee? Label-Free Early Warning on Evolving Graphs](https://mlsanity.com/paper/2610.16534/) (2026)

## Abstract (arXiv)

Concept drift detection is a crucial task in data stream evolving environments. Most of state of the art approaches designed to tackle this problem monitor the loss of predictive models. However, this approach falls short in many real-world scenarios, where the true labels are not readily available to compute the loss. In this context, there is increasing attention to approaches that perform concept drift detection in an unsupervised manner, i.e., without access to the true labels. We propose a novel approach to unsupervised concept drift detection based on a student-teacher learning paradigm. Essentially, we create an auxiliary model (student) to mimic the behaviour of the primary model (teacher). At run-time, our approach is to use the teacher for predicting new instances and monitoring the mimicking loss of the student for concept drift detection. In a set of experiments using 19 data streams, we show that the proposed approach can detect concept drift and present a competitive behaviour relative to the state of the art approaches.