DDG-DA: Data Distribution Generation for Predictable Concept Drift Adaptation. (arXiv:2201.04038v1 [cs.LG])

In many real-world scenarios, we often deal with streaming data that is
sequentially collected over time. Due to the non-stationary nature of the
environment, the streaming data distribution may change in unpredictable ways,
which is known as concept drift. To handle concept drift, previous methods
first detect when/where the concept drift happens and then adapt models to fit
the distribution of the latest data. However, there are still many cases that
some underlying factors of environment evolution are predictable, making …

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