Material : Learning an Invariant Hilbert Space for Domain Adaptation
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چکیده
L = Ld + λLu . (1) In Eq. 1, Ld is a measure of dissimilarity between labeled samples. The term Lu quantifies a notion of statistical difference between the source and target samples in the latent space. In brief, the cost Ld was based on the proposed generalized soft-margin loss, `β on labeled pairs. The statistical loss, Lu was based on Stein divergence, δs between source and target domain covariances in the latent space. Here, we intend to derive the derivative of the proposed `β and Lu. Note that all the variable dimensions and the notations are similar to what we have used in the main text.
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تاریخ انتشار 2017