Titlebook: Domain Adaptation in Computer Vision Applications; Gabriela Csurka Book 2017 Springer International Publishing AG 2017 Computer Vision.Vis

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https://doi.org/10.1007/978-3-031-34398-8rning and DA techniques, and we study their generalization properties to parts from unseen classes when they are learned from a limited number of domains and example images. One of our conclusions is that, for a majority of the domains, part annotations transfer well, and that, performance of the se

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Geodesic Flow Kernel and Landmarks: Kernel Methods for Unsupervised Domain Adaptation hand, we propose . of a kernel that discriminatively combines multiple base GFKs to model the source and the target domains at fine-grained granularities. In particular, each base kernel pivots on a different set of landmarks—the most useful data instances that reveal the similarity between the sou

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Correlation Alignment for Unsupervised Domain Adaptationl but the number and dimensionality of target examples are very high. The resulting CORAL Linear Discriminant Analysis (CORAL-LDA)outperforms LDA by a large margin on standard domain adaptation benchmarks. Finally, we extend CORAL to learn a nonlinear transformation that aligns correlations of layer

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Generalizing Semantic Part Detectors Across Domainsrning and DA techniques, and we study their generalization properties to parts from unseen classes when they are learned from a limited number of domains and example images. One of our conclusions is that, for a majority of the domains, part annotations transfer well, and that, performance of the se