Universal Domain Adaptation(通用域自适应)是指源域的标签空间和目标域的标签空间有一定的交集,同时两者又有各自独立的部分。 在这种情况下,需要对目标域的图片进行分类,若该图片的类别属于源域,则分为该类,否则,标记为不知道(unknown)。 在处理Universal Domain Adaptation问题时,存在两个难点:一是 Category gap,...
基本概念 Universal Domain Adaptation through Self-Supervision 基本概念 定义LS,LT 是DS,DT 的标签空间(set). category shifts,它有三种类别: closed-set: LS=LT . 这也是传统DA的setting,类别数一样。对应 Closed-set Domain Adaptation(CDA) open-set: LS⊂LT . DT 的类别较多。对应 Open-set DA (ODA...
论文代码:GitHub - thuml/Universal-Domain-Adaptation: Code release for Universal Domain Adaptation(CVPR 2019) 论文作者:清华大学龙明盛老师的团队,对这个名字熟悉是因为后来我看的好多篇DA的论文,想到的idea发现他们团队都做过了 论文简介: 这篇论文主要是提出了一种新的问题设置,Domain gap和category gap并存的...
(CVPR 2019)Universal Domain Adaptation 文章链接 本文主要是针对Universal Domain Adaptation问题提出的方法 Universal Domain Adaptation是指目标域的标签空间未知的无监督领域自适应(Unsupervised Domain Adaptation)问题,如下图所示 网络结构 训练部分,图像 x x x输入进入特征提... 查看原文 CV中领域自适应问题 :...
UniversalDomainAdaptationKaichaoYou1,MingshengLong1(),ZhangjieCao1,JianminWang1,andMichaelI.Jordan21KLiss,MOE;BNRist;Scho..
Universal Domain Adaptation (UniDA) aims to transfer knowledge from a source domain to a target domain without any constraints on label sets. Since both domains may hold private classes, identifying target common samples for domain alignment is an essential issue in UniDA. Most existing methods re...
Universal domain adaptation (UniDA) transfers knowledge between domains without any constraint on the label sets, extending the applicability of domain adaptation in the wild. In UniDA, both the source and target label sets may hold individual labels not shared by the other domain. A de facto ...
Universal domain adaptation (UniDA) aims to address domain and category shifts across data sources. Recently, due to more stringent data restrictions, researchers have introduced source-free UniDA (SF-UniDA). SF-UniDA methods eliminate the need for direct access to source samples when performing ...
Universal Domain Adaptation SUMMARY@2020/3/27 文章目录 Motivation Related Work Challenges / Aims /Contribution Method Proposed Feature extractor FFF Label classifier GGG **Non-adversarial** domain discrim...文献阅读笔记—Universal Language Model Fine-tuning for Text Classification 一、问题描述 这是一篇...