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人口历史信息驱动的进化多任务神经架构搜索历史信息驱动的进化多任务神经架构搜索
IEEE transactions on neural networks and learning systems
|December 2, 2025
概括
这项研究引入了一种新的进化多任务神经架构搜索 (MT-NAS) 算法,该算法利用人口历史信息. 这种方法通过防止冗余搜索和减轻任务之间的负面转移来提高效率.
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
- 自然语言处理自然语言处理.
背景情况:
- 神经架构搜索 (NAS) 自动化了神经网络设计,进化NAS非常成功.
- 多任务NAS (MT-NAS) 旨在在计算机视觉和NLP等多个领域实现高效的架构发现.
- 现有的MT-NAS方法遭受冗余的搜索和负转移,由于使用历史数据和未指导的任务交互不善.
研究的目的:
- 提出一种新的进化多任务神经架构搜索 (MT-NAS) 算法.
- 解决现有的MT-NAS方法的局限性,特别是冗余搜索和负转移.
- 通过改进信息利用和任务交互来提高NAS的效率和有效性.
主要方法:
- 开发了一个以人口历史信息为驱动的进化多任务神经架构搜索 (HIMT-NAS) 算法.
- 记录了每一代人口的历史信息 (操作和拓).
- 系统地利用历史信息来指导进化搜索方向,防止冗余的探索.
- 根据从历史信息模式中得出的测量任务相似性的基础上调整的跨任务知识转移概率.
- 当信息在多个任务中证明有益时,动态更新传输概率.
主要成果:
- 拟议的HIMT-NAS算法在单任务NAS和现有的MT-NAS方法上表现出一致的优势.
- 在基准数据集上进行了实验,包括MedMNIST,CIFAR-10,CIFAR-100和Tiny-ImageNet.
- 该方法通过利用人口历史数据有效地减少了冗余搜索.
- 通过基于任务相似性的动态调整概率,通过动态调整概率,实现了跨任务知识转移的改进.
结论:
- 在高效和有效的多任务神经架构搜索中,HIMT-NAS算法提供了显著的进步.
- 利用人口历史信息对于优化NAS效率和减轻负面转移至关重要.
- 拟议的方法为跨越各种AI领域的自动化神经网络设计提供了一个强大的框架.
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