资源受限制的多资源基于实例的转移学习
IEEE transactions on neural networks and learning systems
|November 6, 2023
概括
本研究介绍了MSOPTL,一种用于资源有限环境的新型转移学习 (TL) 模型. 它通过优化数据使用和资源限制来最大限度地提高准确性并减轻负面传输.
科学领域:
- 机器学习 机器学习
- 人工智能的人工智能
- 数据科学数据科学数据科学
背景情况:
- 机器学习 (ML) 需要大量的数据,在资源有限的环境中带来挑战.
- 转移学习 (TL) 解决了数据稀缺问题,但面临着计算/通信障碍和负转移 (NT).
- 当前的TL研究在解决NT时经常忽视资源消耗.
研究的目的:
- 为了最大限度地提高基于实例的TL准确性,在多资源,资源受限制的环境中.
- 通过考虑计算和通信成本来减轻负转移 (NT).
- 引入一种新的优化模型,以实现高效准确的TL.
主要方法:
- 开发了一个多源资源受限优化TL (MSOPTL) 模型.
- MSOPTL使用经验来源和目标错误的凸起组合.
- 通过将Kullback-Leibler (KL) 差异作为可行性约束,增强了概括误差限制.
主要成果:
- 在资源有限的场景中,MSOPTL有效地平衡了TL的好处与相关成本.
- 在基于神经网络 (NN) 的分类任务上验证了模型.
- 证明了提高准确性和减少负面转移.
结论:
- MSOPTL为各种ML方法提供了一个多功能框架,特别是在边缘AI.
- 该方法成功地解决了TL的数据稀缺性和计算挑战.
- 这项工作促进了TL在资源有限的环境中的实际应用.
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