摘要
元学习被认为是一种重要的解决小样本学习任务的工作,但其使用的浅层神经网络具有局限性,最近的工作表明深度神经网络模型虽然具有强大的特征提取能力,但存在一定程度的过度拟合问题,无法快速适应样本稀缺的新任务。为了更好地适应新任务,提出了一种线性迁移的元学习方法,通过对深度神经网络进行大量任务的训练,将其线性迁移到小样本场景中,来缓解过度拟合的问题,并且获得快速适应新任务的能力;随后再通过引入课程学习的分段批操作来进一步提高模型的鲁棒性。实验结果证明该方法在四种小样本数据集Mini-ImageNet、Fewshot-CIFAR100、TieredImageNet和Omniglot上取得了良好的分类效果。
Meta-learning is recognized as an important method for solving few-shot learning tasks,but its use of shallow neural networks has limitations,and recent work has shown that deep neural network models,while powerful in feature extraction,suffer from a certain degree of overfitting problem and are unable to adapt quickly to new tasks where samples are scarce.In order to better adapt to new tasks,a linear meta-transfer method is proposed,which mitigates the overfitting problem and gains the ability to quickly adapt to new tasks by training the deep neural network on a large number of tasks and linearly transferring it to few-shot scenarios.The robustness of the model is then further improved by introducing segmented batch operations based on curriculum learning.Experimental results demonstrate that the method in this paper achieves good classification performance on four base datasets:Mini-ImageNet,Fewshot-CIFAR100,Tiered ImageNet and Omniglot.
作者
王佳恬
李凡长
WANG Jiatian;LI Fanzhang(School of Computer Science and Technology,Soochow University,Suzhou,Jiangsu 215000,China)
出处
《计算机工程与应用》
北大核心
2025年第5期177-186,共10页
Computer Engineering and Applications
基金
国家重点研发计划(2018YFA0701700,2018YFA0701701)
国家自然科学基金(61672364,62176172,61902269)。
关键词
小样本学习
元学习
迁移学习
深度神经网络
课程学习
few-shot learning
meta-learning
transfer learning
deep neural network
curriculum learning
