在实现特征提取后,为了使得源域和目标域在特征空间的距离缩小,作者提出了domain confusion loss来使得这两个域在特征空间难以被识别出来,这就相当于降低了domain shift,进而促使模型学到domain-invariant特征。 不仅如此,作者在这边文章主要做了两件事,其一就是domain transfer,这就是上面提到的用domain confusion loss进...
以数据为中心的方法(data centric methods ) 寻求一个统一的转换,将数据从source domain和target domain投影到域不变空间(domain invariant space)当中,以求减少source domain和target domain上数据的分布差异(distributional divergence),并且同时保留原始空间当中的数据属性 以数据为中心的方法(data centric methods )仅仅...
以数据为中心的方法(data centric methods ) 寻求一个统一的转换,将数据从source domain和target domain投影到域不变空间(domain invariant space)当中,以求减少source domain和target domain上数据的分布差异(distributional divergence),并且同时保留原始空间当中的数据属性 以数据为中心的方法(data centric methods )仅仅...
Discriminative and domain invariant subspace alignment for visual tasksUnsupervised domain adaptationGlobal adaptationLocal adaptationDistinct transformationMaximum mean discrepancyTransfer learning and domain adaptation are promising solutions to solve the problem that the training set (source domain) and the test...
Domain Invariant and Class Discriminative Feature Learning for Visual Domain Adaptation [TIP 2018] Graph Adaptive Knowledge Transfer for Unsupervised Domain Adaptation [ECCV2018] Aligning Infinite-Dimensional Covariance Matrices in Reproducing Kernel Hilbert Spaces for Domain Adaptation [CVPR2018] Unsupervised ...
Domain Invariant and Class Discriminative Feature Learning for Visual Domain Adaptation [TIP 2018] Graph Adaptive Knowledge Transfer for Unsupervised Domain Adaptation [ECCV2018] Aligning Infinite-Dimensional Covariance Matrices in Reproducing Kernel Hilbert Spaces for Domain Adaptation [CVPR2018] Unsupervised ...
featurelearningmethodarepro-posed,bothofwhichguaranteethedomaininvariantfeatureswithbetterintra-classcompactnessandinter-classseparabil-ity.Extensiveexperimentsshowthatlearningthediscrimi-nativefeaturesinthesharedfeaturespacecansignificantlyboosttheperformanceofdeepdomainadaptationmethods.IntroductionDomainadaptation,which...
adapting from MR to CT, and from CT to MR.Contemporary UDA methods attempt to extract domain-invariant representations,Semi-supervised domain adaptation concentrates on an even more annotation-efficient setting, to concurrently leverage the additional supervisions from both cross-modality data and unlabele...
Domain Invariant and Class Discriminative Feature Learning for Visual Domain Adaptation [TIP 2018] Graph Adaptive Knowledge Transfer for Unsupervised Domain Adaptation [ECCV2018] Aligning Infinite-Dimensional Covariance Matrices in Reproducing Kernel Hilbert Spaces for Domain Adaptation [CVPR2018] Unsupervised ...
Domain Invariant and Class Discriminative Feature Learning for Visual Domain Adaptation [TIP 2018] Graph Adaptive Knowledge Transfer for Unsupervised Domain Adaptation [ECCV2018] Aligning Infinite-Dimensional Covariance Matrices in Reproducing Kernel Hilbert Spaces for Domain Adaptation [CVPR2018] Unsupervised ...