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通过数据集相似性通过深度神经网络参数选择在元学习框架下

Liping Deng, Maziar Raissi, MingQing Xiao

IEEE transactions on pattern analysis and machine intelligence

|October 7, 2025

概括

这项研究引入了一个新的元学习框架,通过使用数据集相似性,共同推深度神经网络 (DNN) 的超参数和初始权重. 这种方法提高了深度学习模型的适应性和有效性.

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:

深度神经网络 (DNN) 的性能对超参数选择和重量初始化非常敏感.
当前的方法通常会独立优化这些因素,从而限制模型的适应性和有效性.

研究的目的:

开发一个新的元学习框架,共同推超参数和初始权重.
为了利用数据集相似性来改进深度学习模型的优化.

主要方法:

从历史数据集中提取元特征 (浅层和深层).
在查询数据集的元特征空间中计算数据集相似性.
基于类似的历史数据集推参数配置.

主要成果:

该框架在105个现实世界图像分类任务中进行了评估.
对于视觉变压器和卷积神经网络,在最先进的基线上表现出一致的超越性.
验证了数据集驱动参数推的有效性.

结论:

通过数据集相似性共同推超参数和初始权重是有效的.
拟议的元学习框架提高了深度学习模型的性能.
这种数据集驱动的方法提供了更具适应性和有效的优化策略.

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