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Classical machine learning algorithms often assume that the data are drawni.i.d. from a stationary probability distribution. Recently, continual learningemerged as a rapidly growing area of machine learning where this assumption isrelaxed, i.e. where the data distribution is non-stationary and changes overtime. This paper represents the state of data distribution by a contextvariable $c$. A drift in $c$ leads to a data distribution drift.  A context drift may change the target distribution, the input distribution,or both. Moreover, distribution drifts might be abrupt or gradual. In continuallearning, context drifts may interfere with the learning process and erasepreviously learned knowledge; thus, continual learning algorithms must includespecialized mechanisms to deal with such drifts. In this paper, we aim toidentify and categorize different types of context drifts and potentialassumptions about them, to better characterize various continual-learningscenarios. Moreover, we propose to use the distribution drift framework toprovide more precise definitions of several terms commonly used in thecontinual learning field.

Understanding Continual Learning Settings with Data Distribution Drift Analysis