3、多源对抗域聚合网络 在本节中,我们介绍了所提出的用于语义分割自适应的多源对抗域聚合网络。该框架如图1所示,由三个组成部分组成:动态对抗图像生成、对抗域聚合和特征对齐语义分割。DAIG旨在从视觉外观的角度生成从源域到目标域的自适应图像,同时利用动态分割模型保留语义信息。为了减少自适应域之间的距离,从而生成...
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...
Multi-sourceDomainAdaptationintheDeepLearningEra:ASystematicSurveySichengZhao1,BoLi1,ColoradoReed1,PengfeiXu2,KurtKeutzer11..
To reduce annotation labor associated with object detection, an increasing number of studies focus on transferring the learned knowledge from a labeled source domain to another unlabeled target domain. However, existing methods assume that the labeled data are sampled from a single source domain, which...
A Pytorch Code forMulti-source Domain Adaptation for Semantic Segmentation If you use this code in your research please consider citing: @InProceedings{zhao2019madan, title = {Multi-source Domain Adaptation for Semantic Segmentation}, author = {Zhao, Sicheng and Li, Bo and Yue, Xiangyu and Gu,...
train_source.py README MIT license DECISION Unsupervised Multi-source Domain Adaptation Without Access to Source Data (CVPR '21 Oral) Overview This repository is a PyTorch implementation of the paperUnsupervised Multi-source Domain Adaptation Without Access to Source Datapublished atCVPR 2021. This cod...
Traditional transfer learning models were designed to transfer knowledge from a single source domain to the target domain. In the practical application of biomedical trigger recognition, we can access to datasets from multiple domains. This is also the case in many other applications. Hence, some m...
Transferring knowledges learned from multiple source domains to target domain is a more practical and challenging task than conventional single-source domain adaptation. Furthermore, the increase of modalities brings more difficulty in aligning feature distributions among multiple domains. To mitigate these ...
从结构上来看,这也是个 multi-head 的结构,但蓝色的 head 是 source domain 的原始 task,红色的 head(后面简称为 discriminator) 则是一个 domain classifier,即是用来区分样本是属于哪一个 domain 的两个task 在做 bp 时,蓝色的 head 正常回传梯度,discriminator 则在梯度回传到 feature extractor 即图中色绿...
In this study, a multisource domain adaptation joint-Y partial least square (PLS) method is proposed to learn the similarities between domains and use them to construct a quality prediction model. Without constraints on the number of source and target domains, the proposed method can transfer ...