阶级增量云设备协作适应与动态变化的环境中的对比学习
IEEE transactions on neural networks and learning systems
|February 13, 2026
概括
本研究介绍了类增量云设备协作学习 (CI-CDCL),以改善边缘设备上的轻量级模型概括. 拟议的对比原型网络增强了对新数据类和现有数据类的学习.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 计算机视觉 计算机视觉
背景情况:
- 轻量级模型对于边缘设备至关重要,但在动态环境中难以泛化.
- 云端设备协作学习 (CDCL) 将知识从云端转移到边缘,但当前的方法忽视了不断进入的新类.
- 将课程增量学习 (CIL) 调整为CDCL面临诸如轻量级模型概括性差异和数据增量学习过拟合等挑战.
研究的目的:
- 解决现有的CDCL方法在处理不断新增类的局限性.
- 提出一个新的框架,用于阶级增量云设备协作学习 (CI-CDCL).
- 提高概括性,减少轻量级模型在动态增量学习场景中的过度拟合.
主要方法:
- 提出了一个基于网络的新型对比原型CI-CDCL框架.
- 该框架旨在提高协作学习的有效性,无论是类增量学习还是数据增量学习.
- 在公共数据集上进行了广泛的实验,以验证模型的性能.
主要成果:
- 拟议的CI-CDCL框架在改善云设备协作方面表现出有效性.
- 该模型解决了不良概括和过度适应在增量学习环境中的关键挑战.
- 实验结果验证了对基准数据集的拟议方法.
结论:
- 开发的CI-CDCL框架为边缘设备的持续学习提供了一个有希望的解决方案.
- 这项工作通过结合类增量学习能力来推进CDCL.
- 对比的原型网络方法提高了轻型模型的适应性和稳定性.
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