论文标题
RSAC：用于轻量级学习的正规化子空间近似分类器
RSAC: Regularized Subspace Approximation Classifier for Lightweight Continuous Learning
论文作者
论文摘要
持续学习试图对不时到达的数据进行学习。尽管先前的工作已经证明了几种可能的解决方案，但这些方法需要过度的训练时间以及记忆使用。对于时间和存储的应用程序，例如边缘计算，这是不切实际的。在这项工作中，提出了一种新颖的培训算法，正规子空间近似分类器（RSAC），以实现轻量级的连续学习。 RSAC包含带有正则化的特征还原模块和分类器模块。广泛的实验表明，RSAC比以前的连续学习工作更有效，并且在各种实验设置上都优于这些作品。
Continuous learning seeks to perform the learning on the data that arrives from time to time. While prior works have demonstrated several possible solutions, these approaches require excessive training time as well as memory usage. This is impractical for applications where time and storage are constrained, such as edge computing. In this work, a novel training algorithm, regularized subspace approximation classifier (RSAC), is proposed to achieve lightweight continuous learning. RSAC contains a feature reduction module and classifier module with regularization. Extensive experiments show that RSAC is more efficient than prior continuous learning works and outperforms these works on various experimental settings.
