Unsupervised Domain Adaptation for Distance Metric Learning
Unsupervised domain adaptation is a promising avenue to enhance the performance of deep neural networks on a target domain, using labels only from a source domain.However, the two predominant methods, domain discrepancy reduction learning and semi-supervised learning, are not readily applicable when source and target domains do not share a common label space.This paper addresses the above scenario by learning a representation space that retains discriminative power on both the source and target domains while keeping representations for the two domains well-separated.Inspired by a theoretical analysis, we first reformulate the disjoint classification task, where the source and target domains correspond to non-overlapping class labels, to a verification one.To handle both within and cross domain verifications, we propose a Feature Transfer Network to separate the target feature space from the original source space while aligned with a transformed source space.Moreover, we present a non-parametric multi-class entropy minimization loss to further boost the discriminative power of FTNs on the target domain.In experiments, we first illustrate how FTN works in a controlled setting of adapting from MNIST-M to MNIST with disjoint digit classes between the two domains and then demonstrate the effectiveness of FTNs through state-of-the-art performances on a cross-ethnicity face recognition problem.
A new theory of unsupervised domain adaptation for distance metric learning and its application to face recognition across diverse ethnicity variations.Proposes a novel feature transfer network that optimizes domain adversarial loss and domain separation loss.