PUnDA: Probabilistic Unsupervised Domain Adaptation for Knowledge Transfer Across Visual Categories

This paper introduces a probabilistic latent variable model to address unsupervised domain adaptation problems. This is achieved by learning projections from each domain to a latent space along the classifier in the latent space to simultaneously minimizing a notion of domain disparity while maximizing a measure of discriminatory power... (read more)

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