無需存儲限制的在線持續學習

摘要

線上持續學習（Online continual learning, OCL）的研究主要集中在減輕災難性遺忘，並在整個代理人的壽命中固定和有限地配置存儲空間。然而，數據存儲成本的不斷降低突顯了許多應用並不遵循這些假設。在這些情況下，主要關注點在於管理計算開支而非存儲。本文針對這種情況，通過放寬存儲限制並強調固定、有限的經濟預算，探討了線上持續學習問題。我們提供了一個簡單的算法，可以在微小的計算預算下緊湊存儲和利用整個傳入數據流，使用k最近鄰（kNN）分類器和通用預訓練特徵提取器。我們的算法提供了一個對持續學習有吸引力的一致性特性：它永遠不會忘記過去看到的數據。我們在兩個大規模的線上持續學習數據集上設立了一個新的技術水準：Continual LOCalization（CLOC）數據集包含了712個類別的3900萬張圖像，以及Continual Google Landmarks V2（CGLM）數據集包含了10788個類別的58萬張圖像。我們的方法在減少過去數據的災難性遺忘和快速適應快速變化的數據流方面，勝過了在計算預算遠高於我們的方法。我們提供了代碼以重現我們的結果，網址為https://github.com/drimpossible/ACM。

English

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 https://github.com/drimpossible/ACM.

無需存儲限制的在線持續學習

Online Continual Learning Without the Storage Constraint

摘要

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