… but challenging problem, Online Continual Learning, where data arrive one … learn from
a single pass over the online data stream where the model may experience new classes (Online …

… In this paper, we prove that in the online continual learning setting we can smartly select
a finite number of data to be representative of all previously seen data without knowing task …

… online prototype learning (OnPro) framework for online CL. First, we propose online prototype
equilibrium to learn representative features against shortcut learning and discriminative …

… This paper proposes a new online continual learning technique called OCM based on
mutual information maximization. It achieves two objectives that are critical in dealing with …

… learning epochs without any time limitation when a task is added. We argue that “online”
continual learning… enables evaluating both information retention and online learning efficacy. In …

… In this work, we consider an online continual setting where a stream of samples is seen only
… Since we assume an online continual learning setting, we need to address learning the …

… Online Learning (OL) studies optimization methods which can operate with a stream of data:
learning … task, in this paper we deal with Online Continual Learning (OCL), where data are …

… We start with the classical problem statement for online continual learning. Then, we
formally introduce our proposed real-time evaluation that factors in training complexity through …

… To encourage progress in Online Continual Learning, in this paper, we provide a comprehensive
empirical evaluation of OCL methods. We exploit recent proposals that provide better …

… Recent work in the field of continual learning attempts to … real world, humans and animals
learn from observa tions that are … that are used in online continual learning, when dealing with …