Position Paper: A new Perspective on Online Continual Learning

We consider the setting of online/streaming continual learning. In particular, we analyze the implications of the consolidated continual learning evaluation setting in an online scenario. As the data stream potentially extends infinitely, the evaluation set also grows boundlessly, necessitating a redefinition of model evaluation. In the streaming learning literature, the model is usually evaluated with the test-then-train method that does not require a separate evaluation set at all, or with continuous reevaluation that considers partially delayed labels [1]. In this work, we propose to model the data-generating process as random walks on concept graphs, i.e. graphs that define the possible transitions between the concepts to learn. This offers a precise formalization of the problem of learning on (possibly) infinitely long data streams with concept drifts and recurring concepts. In such a setting, we also discuss alternative possibilities that blend the continual learning setting with the traditional online learning one. We propose a framework that generalizes the commonly considered online continual learning scenario (i.e. corresponds to a specific topology of the concept graph). Finally, we discuss how different topologies of data streams, derived from our framework, could inspire new research directions in continual learning.