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Jul, 2022
连续学习中任务数量的缩放
Scaling the Number of Tasks in Continual Learning
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Timothée Lesort, Oleksiy Ostapenko, Diganta Misra, Md Rifat Arefin, Pau Rodríguez...
TL;DR
本文探讨了在有限环境中增长任务数量的情景下,通过一种新的实验框架SCoLe,借助随机梯度下降法,实现在长序列的任务中进行知识积累和保留,提出了一种改进的随机梯度下降方法以便于在此设置中进行继续学习的算法,并通过合适的实验框架展示了新的可持续性学习学习机制。
Abstract
Standard gradient descent algorithms applied to sequences of tasks are known to produce
catastrophic forgetting
in
deep neural networks
. When trained on a new task in a sequence, the model updates its parameters
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