Online Continual Learning: A Systematic Literature Review of Approaches, Challenges, and Benchmarks

TL;DR

This paper provides the first comprehensive systematic review of online continual learning, analyzing 81 approaches, datasets, and challenges, and proposing future research directions to address key issues like scalability and domain-agnostic solutions.

Contribution

It offers a detailed synthesis of existing OCL approaches, features, components, datasets, and challenges, along with identifying promising future research directions.

Findings

Key challenges include reducing computational overhead and improving scalability.

Identification of promising directions such as self-supervised learning and adaptive memory mechanisms.

Comprehensive analysis of 81 approaches and 83 datasets in OCL.

Abstract

Online Continual Learning (OCL) is a critical area in machine learning, focusing on enabling models to adapt to evolving data streams in real-time while addressing challenges such as catastrophic forgetting and the stability-plasticity trade-off. This study conducts the first comprehensive Systematic Literature Review (SLR) on OCL, analyzing 81 approaches, extracting over 1,000 features (specific tasks addressed by these approaches), and identifying more than 500 components (sub-models within approaches, including algorithms and tools). We also review 83 datasets spanning applications like image classification, object detection, and multimodal vision-language tasks. Our findings highlight key challenges, including reducing computational overhead, developing domain-agnostic solutions, and improving scalability in resource-constrained environments. Furthermore, we identify promising directions for future research, such as leveraging self-supervised learning for multimodal and sequential data, designing adaptive memory mechanisms that integrate sparse retrieval and generative replay, and creating efficient frameworks for real-world applications with noisy or evolving task boundaries. By providing a rigorous and structured synthesis of the current state of OCL, this review offers a valuable resource for advancing this field and addressing its critical challenges and opportunities. The complete SLR methodology steps and extracted data are publicly available through the provided link: https://github.com/kiyan-rezaee/ Systematic-Literature-Review-on-Online-Continual-Learning