# DriftGAN: Using historical data for Unsupervised Recurring Drift Detection

arXiv: https://arxiv.org/abs/2407.06543 · HTML: https://mlsanity.com/paper/2407.06543/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 21 references that are in the map.
- First posted on arXiv 9 Jul 2024.

## Builds on (most cited first, 6 of 6)

- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [MelGAN: Generative Adversarial Networks for Conditional Waveform Synthesis](https://mlsanity.com/paper/1910.06711/) (2019)
- [RankSRGAN: Generative Adversarial Networks with Ranker for Image Super-Resolution](https://mlsanity.com/paper/1908.06382/) (2019)
- [Scikit-Multiflow: A Multi-output Streaming Framework](https://mlsanity.com/paper/1807.04662/) (2018)
- [Efficient and Accurate MRI Super-Resolution using a Generative Adversarial Network and 3D Multi-Level Densely Connected Network](https://mlsanity.com/paper/1803.01417/) (2018)
- [The Importance of Future Information in Credit Card Fraud Detection](https://mlsanity.com/paper/2204.05265/) (2022)

## Built on by (most cited first, 2 of 2)

- [SUDS: A Strategy for Unsupervised Drift Sampling](https://mlsanity.com/paper/2411.02995/) (2024)
- [Describing Nonstationary Data Streams in Frequency Domain](https://mlsanity.com/paper/2502.04813/) (2025)

## Abstract (arXiv)

In real-world applications, input data distributions are rarely static over a period of time, a phenomenon known as concept drift. Such concept drifts degrade the model's prediction performance, and therefore we require methods to overcome these issues. The initial step is to identify concept drifts and have a training method in place to recover the model's performance. Most concept drift detection methods work on detecting concept drifts and signalling the requirement to retrain the model. However, in real-world cases, there could be concept drifts that recur over a period of time. In this paper, we present an unsupervised method based on Generative Adversarial Networks(GAN) to detect concept drifts and identify whether a specific concept drift occurred in the past. Our method reduces the time and data the model requires to get up to speed for recurring drifts. Our key results indicate that our proposed model can outperform the current state-of-the-art models in most datasets. We also test our method on a real-world use case from astrophysics, where we detect the bow shock and magnetopause crossings with better results than the existing methods in the domain.