Online continual learning (OCL) research has primarily focused on mitigating catastrophic forgetting with fixed and limited storage allocation throughout the agent's lifetime. However, the growing affordability of data storage highlights a broad range of applications that do not adhere to these assumptions. In these cases, the primary concern lies in managing computational expenditures rather than storage. In this paper, we target such settings, investigating the online continual learning problem by relaxing storage constraints and emphasizing fixed, limited economical budget. We provide a simple algorithm that can compactly store and utilize the entirety of the incoming data stream under tiny computational budgets using a kNN classifier and universal pre-trained feature extractors. Our algorithm provides a consistency property attractive to continual learning: It will never forget past seen data. We set a new state of the art on two large-scale OCL datasets: Continual LOCalization (CLOC), which has 39M images over 712 classes, and Continual Google Landmarks V2 (CGLM), which has 580K images over 10,788 classes -- beating methods under far higher computational budgets than ours in terms of both reducing catastrophic forgetting of past data and quickly adapting to rapidly changing data streams. We provide code to reproduce our results at \url{https://github.com/drimpossible/ACM}.

翻译：在线持续学习（OCL）研究主要聚焦于在智能体整个生命周期内，通过固定且有限的存储分配来减轻灾难性遗忘。然而，数据存储成本日益降低，凸显出大量不符合上述假设的应用场景。在这些情况下，主要问题在于管理计算开销而非存储。本文针对此类场景展开研究，通过放宽存储限制并强调固定且有限的经济预算来探索在线持续学习问题。我们提出了一种简单算法，该算法能够在极小的计算预算下，利用k近邻分类器和通用预训练特征提取器，紧凑地存储并利用全部传入数据流。该算法具有对持续学习颇具吸引力的一致性特性：它永远不会遗忘过去已见过的数据。我们在两个大规模OCL数据集上取得了新的最优结果：包含712个类别共3900万张图像的持续定位（CLOC）数据集，以及包含10788个类别共58万张图像的持续谷歌地标V2（CGLM）数据集——在计算预算远低于其他方法的情况下，在减少过去数据灾难性遗忘和快速适应急剧变化数据流两方面均超越了这些方法。我们提供代码以复现实验结果，代码地址为：\url{https://github.com/drimpossible/ACM}。