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

Behnam Gholami, Ognjen (Oggi) Rudovic, Vladimir Pavlovic; The IEEE International Conference on Computer Vision (ICCV), 2017, pp. 3581-3590

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.

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[bibtex]
@InProceedings{Gholami_2017_ICCV,
author = {Gholami, Behnam and (Oggi) Rudovic, Ognjen and Pavlovic, Vladimir},
title = {PUnDA: Probabilistic Unsupervised Domain Adaptation for Knowledge Transfer Across Visual Categories},
booktitle = {The IEEE International Conference on Computer Vision (ICCV)},
month = {Oct},
year = {2017}
}