Self-adaptive label filtering learning for unsupervised domain adaptation

被引：0

作者：

Qing TIAN ^{[1
,2
]}

Heyang SUN ^{[1
]}

Shun PENG ^{[1
]}

Tinghuai MA ^{[1
,2
]}

机构：

[1] School of Computer and Software,Nanjing University of Information Science and Technology

[2] Engineering Research Center of Digital Forensics,Ministry of Education,Nanjing University of Information Science and

来源：

Frontiers of Computer Science | 2023年 / 17卷 / 01期

关键词：

D O I：

暂无

中图分类号：

TP181 [自动推理、机器学习];

学科分类号：

摘要：

<正>1 Introduction As an emerging machine learning paradigm,unsupervised domain adaptation (UDA) aims to train an effective model for unlabeled target domain by leveraging knowledge from related but distribution-inconsistent source domain.Most of the existing UDA methods [2] align class-wise distributions resorting to target domain pseudo-labels,for which hard labels may be misguided by misclassifications while soft labels are confusing with trivial noises so that both of them tend to cause frustrating performance.

引用

页码：222 / 224

页数：3

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