unknown-aware domain adversarial learning for open-set domain adaptation 是一种针对开集域适应问题的域对抗学习方法,旨在提高模型对未知类别的识别能力。 在open-set domain adaptation(开集域适应)问题中,目标域包含源域中不存在的类别(未知类别)。传统的域适应方法在处理这类问题时,往往难以有效识别这些未知类别,...
Open Set Domain Adaptation学习笔记 方法概述 Busto等人在提出开集域适应任务的同时,也提出了迭代分配变换(Assign-and Transform-Iteratively,ATI)方法。该方法不但在新提出的开集域适应问题中取得良好成效,而且还适用于传统的闭集域适应问题。
联合适应网络(Joint Adaptation Network (JAN)):匹配源域和目标域的特征和标签的联合分布。 领域对抗神经网络(Domain Adversarial Neural Network (DANN))、对抗性区分域适应(Adversarial Discriminative Domain Adaptation (ADDA)):使用领域鉴别器来区分两个领域,同时学习特征提取器来混淆领域对抗训练范例中的领域鉴别器 ...
Open-Set Domain Adaptation (OSDA) aims to adapt the model trained on a source domain to the recognition tasks in a target domain while shielding any distractions caused by open-set classes, i.e., the classes "unknown" to the source model. Compared to standard DA, the key of OSDA lies ...
Towards Inheritable Models for Open-Set Domain Adaptation笔记 方法概述 现有方法都假设可以访问一个已标注的源域样本集。然而在有些时候,源域由于专有性质或隐私问题,样本的使用会受到限制,例如在医疗、生物等一些特殊的行业,有些敏感数据是不能被公布出来的。源域样本与目标域样本共存的依赖无法满足,现有方法也就...
Open-set domain adaptation (OSDA)是指在目标域存在未知类别时的领域适应问题。 DAOD(Domain Adaptation with Outlier Detection)是一种OSDA方法,它通过引入异常检测器来解决未知类别问题。DAOD包括两个阶段:特征提取和异常检测。在特征提取阶段,源域和目标域之间的特征差异被学习和调整,以实现域自适应。在异常检测...
这篇文章是来自AAAI 2021的《Balanced Open Set Domain Adaptation via Centroid Alignment》[1],面向open set情况下的domain adapation(OSDA)问题。 在常见的分类任务中,open set表示test set包含了一些train set中没有见到过的类别,即C_{train} \subset C_{test},这里的C表示集合中包含的类别;domain adaptation...
域自适应(Domain Adaptation)论文和代码- Universal Domain Adaptation 热度: 领域自适应学习论文Deep Reconstruction-Classification Networks for Unsupervised Domain Adaptation 热度: 8 1 0 2 Ku J 6 OpenSetDomainAdaptationbyBackpropagation KuniakiSaito1,ShoheiYamamoto1,YoshitakaUshiku1,andTatsuyaHarada1,21TheUniver...
However, factors present in non-controllable environments such as unlabeled datasets with varying levels of domain and category shift can reduce model accuracy. The Open Set Domain Adaptation (OSDA) is a challenging problem that arises when both of these issues occur together. Existing OSDA ...
Paper tables with annotated results for KRADA: Known-region-aware Domain Alignment for Open-set Domain Adaptation in Semantic Segmentation