(2) 我们设计了一个名为MADAN的新框架来进行语义分割的MDA。除了特征级对齐之外,还通过为每个源循环生成一个自适应域来进一步考虑像素级对齐,该域与新的动态语义一致性损失保持一致。提出了子域聚合鉴别器和跨域循环鉴别器,以更好地对齐不同的自适应域。(3) 我们从合成的GTA和SYNTHIA到真实的Cityscapes和BDDS数据...
However, there is often more than one source domain in the real-world application to be exploited for DA. In this paper, we formally propose a more general domain adaptation setting for image classification, universal multi-source DA (UMDA), where the label sets of multiple source domains ...
In recent years, domain adaptation and transfer learning are known as promising techniques with admirable performance to deal with problems with distribution difference between the training (source domain) and test (target domain) data. In this paper, a novel unsupervised multi-source transductive ...
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 ...
域适应(domain adaptation)是transfer learning 下的一个子问题,它将问题A具体化为如下的问题:对于一个目标任务(target task),我们收集了一批目标域(target domain)的无标签数据和源域(source domain)的有标签数据, 如何利用这些数据得到一个可接受的模型 for 目标任务。 由于源域和目标域的数据分布不同,所以直接...
Multi-Source Domain Adaptation (MSDA) aims at training a classification model that achieves small target error, by leveraging labeled data from multiple source domains and unlabeled data from a target domain. The source and target domains are described by related but different joint distributions, whi...
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
This paper treats this task as a multi-source domain adaptation and label unification (mDALU) problem and proposes a novel method for it. Our method consists of a partially-supervised adaptation stage and a fully-supervised adaptation stage. In the former, partial knowledge is transferred from ...
Domain Adaptation(域自适应):这是一种迁移学习的方法,它主要利用有着丰富监督信息的source域样本,来提升target域模型的性能。换句话说其解决的问题就是源域与目标域分布不一样的时候怎么样使在源域训练的模型放在目标域模型进行使用时性能更好或者持平。其主要有三种方向:样本层面,加权重采样即源域相对于目标域比较...