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Updated: Sep 12, 2025

Cross-Modal Multivariate Pattern Analysis
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Published on: November 9, 2011

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基于家庭的持续学习,用于与GCN和ViT联合框架中的多领域模式分析

Saeed Iqbal1, Xiaopin Zhong1, Muhammad Attique Khan2

  • 1College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen, 518060, China.

Neural networks : the official journal of the International Neural Network Society
|August 7, 2025
PubMed
概括

本研究介绍了FedCL,这是一个新的框架,使用图形卷积网络和视觉转换器集成联合学习 (FL) 和持续学习 (CL). 它有效地减轻了灾难性遗忘,并在动态的,分散的环境中增强了模型的适应性.

关键词:
灾难性的遗忘.持续的学习 持续的学习基于家庭的持续学习.联邦连续学习学习.图表卷积网络 (GCNs) 的图表.视觉变压器 (ViT) 是一种视觉变压器.

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