ADAPTIVE MEMORY UPDATE MECHANISM FOR MITIGATING CATASTROPHIC FORGETTING AND OPTIMIZING MEMORY UTILIZATION IN TEXT-BASED CONTINUAL LEARNING
DOI:
https://doi.org/10.48047/1eh33p27Keywords:
Continual Learning, Catastrophic Forgetting, Adaptive memory mechanism, Deep learning, Natural Language processing, dynamic memory allocation.Abstract
Catastrophic forgetting, inefficient memory utilization and task adaptation are problems in continual learning in textbased datasets. In this research, in order to overcome these challenges, the proposed Adaptive Memory Update Mechanism (AMUM) serves as a framework for dealing with these challenges in a reasonable manner.
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References
Szegedy, Balázs, Domonkos Czifra, and Péter Kőrösi-Szabó. "Dynamic Memory Based Adaptive Optimization." arXiv preprint arXiv:2402.15262 (2024). 2. Yao, Xuanrong, Xin Wang, Yue Liu, and Wenwu Zhu. "Continual recognition with adaptive memory update." ACM Transactions on Multimedia Computing, Communications and Applications 19, no. 3s (2023): 1-15.
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