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

Abstract

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. The non-parametric nature of our Latent variable model makes it possible to infer the latent space dimension automatically from data. We also develop a Variational Bayes (VB) algorithm for parameter estimation. We evaluate and contrast our proposed model against state-of-the-art methods for the task of visual domain adaptation using both handcrafted and deep net features. Our experiments show that even with a simple softmax classifier, our model can outperform several state-of-the-art methods taking advantage of more sophisticated classification schemes.