  Classical machine learning algorithms often assume that the data are drawn
i.i.d. from a stationary probability distribution. Recently, continual learning
emerged as a rapidly growing area of machine learning where this assumption is
relaxed, namely, where the data distribution is non-stationary, i.e., changes
over time. However, data distribution drifts may interfere with the learning
process and erase previously learned knowledge; thus, continual learning
algorithms must include specialized mechanisms to deal with such distribution
drifts. A distribution drift may change the class labels distribution, the
input distribution, or both. Moreover, distribution drifts might be abrupt or
gradual. In this paper, we aim to identify and categorize different types of
data distribution drifts and potential assumptions about them, to better
characterize various continual-learning scenarios. Moreover, we propose to use
the distribution drift framework to provide more precise definitions of several
terms commonly used in the continual learning field.
