Online Continual Learning: A Systematic Literature Review of Approaches, Challenges, and Benchmarks
Seyed Amir Bidaki, Amir Mohammadkhah, Kiyan Rezaee, Faeze Hassani,, Sadegh Eskandari, Maziar Salahi, Mohammad M. Ghassemi

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…
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Taxonomy
TopicsOnline and Blended Learning
MethodsSurrogate Lagrangian Relaxation
