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

atopic-rhinitis

xanthelasma

nonplus

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

Middle-Ear

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

财政

majestic

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

配偶

pantomime

GENRE

Middle-Ear

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