在第一种策略中,确定如何为不同的适应域选择权重是一项挑战。此外,每个目标图像都需要在参考时间被输入到所有分割模型中,这是相当低效的。对于第二种策略,由于对齐空间是高维的,尽管适应的域与目标相对对齐,但它们可能彼此明显不对齐。为了缓解这个问题,我们提出了对抗性域聚合,用两种鉴别器使不同的适应域更紧密地聚...
Domain adaptation (DA)Multi-source DAPanoramic semantic segmentationDeformation transformationUnsupervised domain adaptation methods for panoramic semantic segmentation utilize real pinhole images or low-cost synthetic panoramic images to transfer segmentation models to real panoramic images. However, these methods...
【Pattern Recognition 2023】迁移学习-多源领域自适应(Multi-Source Domain Adaptation)论文讲解 Riemannian representation learning for multi-source domain adaptation 论文链接:https://www.sciencedirect.com/science/article/pii/S0031320322007506 代码链接:https://github.com/sentaochen/Riemannian-Representation-Learning...
[论文速览]:多源异质域适应 Multi-source Heterogeneous Domain Adaptation,程序员大本营,技术文章内容聚合第一站。
今天介绍一篇关于行人重识别UDA的方法,“Unsupervised Multi-Source Domain Adaptation for Person Re-Identifification ” 论文地址: https://arxiv.org/pdf/2104.12961.pdfarxiv.org/pdf/2104.12961.pdf Motion 以前UDA的方法,都是从一个标记了数据的域获取信息来适应另一个无标签的域。但是reid发展至今,有...
Domain adaptationSoft parameter sharingDeep domain confusionCross-domain sentiment classificationCross-domain sentiment classification uses knowledge from source domain tasks to enhance the sentiment classification of the target task. It can reduce the workload of data annotations in the new domain, and ...
@article{ahmed2021unsupervised, title={Unsupervised Multi-source Domain Adaptation Without Access to Source Data}, author={Ahmed, Sk Miraj and Raychaudhuri, Dripta S and Paul, Sujoy and Oymak, Samet and Roy-Chowdhury, Amit K}, journal={arXiv preprint arXiv:2104.01845}, year={2021} }...
Optimal Transport for Multi-source Domain Adaptation under Target Shift 这篇论文在 Optical Transport 解决域适应的基础上,提出了域间类别不平衡的问题并加以解决。 (a)图是未做 Optical Transport 之前,…
Deep Cocktail Network: Multi-source Unsupervised Domain Adaptation with Category Shift Ruijia Xu1,†, Ziliang Chen1,†, Wangmeng Zuo2, Junjie Yan3, Liang Lin1,3∗ 1Sun Yat-sen University 2Harbin Institute of Technology 3SenseTime Research xurj3@mail2.sysu.edu.cn, c.ziliang@yahoo.com,...
Multi-Source Domain Adaptation (MSDA) focuses on transferring the knowledge from multiple source domains to the target domain, which is a more practical and challenging problem compared to the conventional single-source domain adaptation. In this problem, it is essential to utilize the labeled source...