we propose an unsupervised domain-adaptation approach to 3D reconstruction where labelled images only exist in our source synthetic domain,and training is supplemented with different unlabelled datasets from the
Switzerland ismail.nejjar@epfl.ch Qin Wang ETH Zurich, Switzerland qwang@ethz.ch Olga Fink EPFL, Switzerland olga.fink@epfl.ch Abstract Unsupervised Domain Adaptation Regression (DAR) aims to bridge the domain gap between a labeled source dataset and an...
To solve this problem, we propose an unsupervised feature alignment domain adaptation regression (UFADAR) based soft sensor modeling method. By reducing the feature subspace distance between the source domain and the target domain and introducing a cycle-consistent adversarial network to ensure the ...
Representation subspace distance for domain adaptation regression. Proc 38th Int Conf on Machine Learning, p.1749–1759. Courty N, Flamary R, Habrard A, et al., 2017. Joint distribution optimal transportation for domain adaptation. Proc 31st Int Conf on Neural Information Processing Systems, p....
Learning to Relate Depth and Semantics for Unsupervised Domain Adaptation Suman Saha* ETH Zurich Anton Obukhov* Danda Pani Paudel Menelaos Kanakis ETH Zurich ETH Zurich ETH Zurich Stamatios Georgoulis ETH Zurich Luc Van Gool ETH Zurich, KU Leuven Yuhua Chen ETH Zuri...
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Domain Separation Networks 热度: domain adaptation in regression:回归域自适应 热度: 相关推荐 MaximumClassifierDiscrepancyforUnsupervisedDomainAdaptation KuniakiSaito 1 ,KoheiWatanabe 1 ,YoshitakaUshiku 1 ,and...
Domain adaptationlearning is proposed as an effective technology for leveraging rich supervision knowledge from the related domain(s) to learn a reliable classifier for a new domain. One popular kind of domain adaptation methods is based on feature representation. However, such methods fail to conside...
In continuous unsupervised domain adaptation (CUDA), deep learning models struggle with the stability-plasticity trade-off—where the model must forget old knowledge to acquire new one. This paper introduces the “Forget to Learn” (F2L), a novel framework that circumvents such a trade-off. In...
Cortes C, Mohri M (2014) Domain adaptation and sample bias correction theory and algorithm for regression. Theor Comput Sci 519:103–126 Article MathSciNet Google Scholar Courty N, Flamary R, Amaury H, Rakotomamonjy A (2017) Joint distribution optimal transportation for domain adaptation. In...
Unsupervised Domain Adaptation (UDA) is a popular machine learning technique to reduce the distribution discrepancy among domains. Generally, most UDA methods utilize a deep Convolutional Neural Networks (CNNs) and a domain discriminator to learn a domain-invariant representation, but it does not equal...