两个不同的子任务分别产生伪标签,并通过协同训练相互学习。 Cross-Domain Adaptive Clustering for Semi-Supervised Domain Adaptation一文中通过测量成对特征相似性将目标特征分组。 Multi-level Consistency Learning for Semi-supervised Domain Adaptation一文中利用三个不同级别的一致性正则化执行域对齐。 此外,这两篇文...
论文链接:Semi-Supervised Domain Adaptation via Minimax Entropy,ICCV2019 代码链接:github.com/VisionLearni 是早期半监督学习和域适应结合的工作,主要解决如何使算法在不同数据域之间具备迁移能力的问题。这个方向个人觉得有些小众,因为在实际应用场景很少有符合定义的数据集,大部分都是unsupervised domain adaptation(UDA...
论文链接:Semi-Supervised Domain Adaptation via Minimax Entropy, 2019 ICCV 项目链接:https://cs-people.bu.edu/keisaito/research/MME.html里面有代码库 一、问题设定与背景: SSDA:半监督域适应,即有标签源域、少量有标签目标域+大量无标签目标域 问题:无标签域适应不需要来自目标域的监督,通过对齐源域和目标...
Semi-supervised domain adaptationDomain adaptation is a potential method to train a powerful deep neural network across various datasets. More precisely, domain adaptation methods train the model on training data and test that model on a completely separate dataset. The adversarial-based adaptation ...
论文标题:Semi-supervised Domain Adaptation in Graph Transfer Learning论文作者:论文来源:2024 aRxiv论文地址:download 论文代码:download视屏讲解:click 1-摘要作为图转移学习的一个特殊情况,图上的无监督域自适应的目的是将知识从标签丰富的源图转移到未标记的目标图。然而,具有拓扑结构和属性的图通常具有相当大的...
In this regard, self-training techniques based on pseudo-labeling have been shown to be highly effective for semi-supervised domain adaptation. However, the unreliability of pseudo labels can hinder the capability of self-training ... N Ghamsarian,JG Tejero,P Márquez-Neila,... - 《Arxiv》 ...
semi-supervised adaptation where the target domain is partially labeled, and (ii) multi-domain adaptation where there could be more than one domain in sou... R Gopalan,R Li,R Chellappa - 《IEEE Transactions on Pattern Analysis & Machine Intelligence》 被引量: 103发表: 2013年 Semi-Supervise...
Domain adaptation (DA) is the topical problem of adapting models from labelled source datasets so that they perform well on target datasets where only unlabelled or partially labelled data is available. Many methods have been proposed to address this pro
Multi-level Consistency Learning for Semi-supervised Domain Adaptationarxiv.org/abs/2205.04066v3 本文提出了一种用于 SSDA 的多级一致性学习 (MCL) 框架。具体来说,本文的 MCL 在三个级别上规范了目标域样本的不同视图的一致性:(i)在域间级别,本文使用基于原型的优化传输方法,利用目标样本不同视图的优缺点...
2. Multi-level Consistency Learning for Semi-supervised Domain Adaptation SSDA参考文献阅读笔记 1. 这篇文章的标题? 2. 作者、单位、发表刊物、水平? Multi-level Consistency Learning for Semi-supervised Domain Adaptation IJCAI,2022CCFAZizheng Yan1∗ , Yushuang Wu1∗ , Guanbin Li2,1† , Yipeng...