Publication Type

PhD Dissertation

Version

publishedVersion

Publication Date

6-2026

Abstract

Continual learning, also termed lifelong learning, enables machine learning models to incrementally acquire new knowledge while mitigating the degradation of previously learned information—a capability essential for adapting to dynamic, real-world data environments. This dissertation investigates the core challenges of continual learning and extends its application to enhancing training efficiency in the era of foundation models. The first part of this dissertation addresses the constraints of few-shot exemplar storage with a novel compression framework. While leveraging class activation maps to downsample non-discriminative pixels, we introduce an adaptive masking model, optimized through bilevel optimization, to store more exemplars efficiently. The second part focuses on the data dynamics of continual learning. We model continuous distribution shifts using bell-shaped curves to simulate realistic streams. We identify the challenges of non-stationarity and class imbalance in such evolving environments, and propose a rate-dependent coreset selector for adaptive memory selection to address them. The final part discusses how continual learning (CL) principles can be leveraged to optimize the training lifecycle of foundation models, which have huge capacity but are also susceptible to catastrophic forgetting and prohibitive retraining costs.

Degree Awarded

PhD in Computer Science

Discipline

Artificial Intelligence and Robotics | Computer Sciences

Supervisor(s)

SUN, Qianru

First Page

1

Last Page

91

Publisher

Singapore Management University

City or Country

Singapore

Copyright Owner and License

Author

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