  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.
