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Traditional online continual learning (OCL) research has primarily focused onmitigating catastrophic forgetting with fixed and limited storage allocationthroughout an agent's lifetime. However, a broad range of real-worldapplications are primarily constrained by computational costs rather thanstorage limitations. In this paper, we target such applications, investigatingthe online continual learning problem under relaxed storage constraints andlimited computational budgets. We contribute a simple algorithm, which updatesa kNN classifier continually along with a fixed, pretrained feature extractor.We selected this algorithm due to its exceptional suitability for onlinecontinual learning. It can adapt to rapidly changing streams, has zerostability gap, operates within tiny computational budgets, has low storagerequirements by only storing features, and has a consistency property: It neverforgets previously seen data. These attributes yield significant improvements,allowing our proposed algorithm to outperform existing methods by over 20% inaccuracy on two large-scale OCL datasets: Continual LOCalization (CLOC) with39M images and 712 classes and Continual Google Landmarks V2 (CGLM) with 580Kimages and 10,788 classes, even when existing methods retain all previouslyseen images. Furthermore, we achieve this superior performance withconsiderably reduced computational and storage expenses. We provide code toreproduce our results at github.com/drimpossible/ACM.

Online Continual Learning Without the Storage Constraint