A Unified and General Framework for Continual Learning
Zhenyi Wang Yan Li Li Shen Heng Huang
Continual Learning (CL) focuses on learning from dynamic and changing data
distributions while retaining previously acquired knowledge. Various methods
have been developed to address the challenge of catastrophic forgetting,
including regularization-based, Bayesian-based, and memory-replay-based
techniques. However, these methods lack a unified framework and common
terminology for describing their approaches. This research aims to bridge this
gap by introducing a comprehensive and overarching framework that encompasses
and reconciles these existing methodologies. Notably, this new framework is
capable of encompassing established CL approaches as special instances within a
unified and general optimization objective. An intriguing finding is that
despite their diverse origins, these methods share common mathematical
structures. This observation highlights the compatibility of these seemingly
distinct techniques, revealing their interconnectedness through a shared
underlying optimization objective. Moreover, the proposed general framework
introduces an innovative concept called refresh learning, specifically designed
to enhance the CL performance. This novel approach draws inspiration from
neuroscience, where the human brain often sheds outdated information to improve
the retention of crucial knowledge and facilitate the acquisition of new
information. In essence, refresh learning operates by initially unlearning
current data and subsequently relearning it. It serves as a versatile plug-in
that seamlessly integrates with existing CL methods, offering an adaptable and
effective enhancement to the learning process. Extensive experiments on CL
benchmarks and theoretical analysis demonstrate the effectiveness of the
proposed refresh learning. Code is available at
\url{https://github.com/joey-wang123/CL-refresh-learning}.