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

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Unsupervised Domain Adaptation Based on Subspace Alignmentpace Alignment (SA). They are based on a mapping function which aligns the source subspace with the target one, so as to obtain a domain invariant feature space. The solution of the corresponding optimization problem can be obtained in closed form, leading to a simple to implement and fast algorithm

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Learning Domain Invariant Embeddings by Matching Distributionsoach to addressing this problem therefore consists of learning an embedding of the source and target data such that they have similar distributions in the new space. In this chapter, we study several methods that follow this approach. At the core of these methods lies the notion of distance between

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MUTED

Correlation Alignment for Unsupervised Domain Adaptationift by aligning the second-order statistics of source and target distributions, without requiring any target labels. In contrast to subspace manifold methods, it aligns the original feature distributions of the source and target domains, rather than the bases of lower-dimensional subspaces. It is al

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Domain-Adversarial Training of Neural Networksutions. Our approach is directly inspired by the theory on domain adaptation suggesting that, for effective domain transfer to be achieved, predictions must be made based on features that cannot discriminate between the training (source) and test (target) domains. The approach implements this idea i