  Concept drift refers to a change in the data distribution affecting the data
stream of future samples. Consequently, learning models operating on the data
stream might become obsolete, and need costly and difficult adjustments such as
retraining or adaptation. Existing methods usually implement a local concept
drift adaptation scheme, where either incremental learning of the models is
used, or the models are completely retrained when a drift detection mechanism
triggers an alarm. This paper proposes an alternative approach in which an
unsupervised and model-agnostic concept drift adaptation method at the global
level is introduced, based on autoencoders. Specifically, the proposed method
aims to ``unlearn'' the concept drift without having to retrain or adapt any of
the learning models operating on the data. An extensive experimental evaluation
is conducted in two application domains. We consider a realistic water
distribution network with more than 30 models in-place, from which we create
200 simulated data sets / scenarios. We further consider an image-related task
to demonstrate the effectiveness of our method.
