Abstract
Domain Adaptation is an actively researched problem in
Computer Vision. In this work, we propose an approach
that leverages unsupervised data to bring the source and
target distributions closer in a learned joint feature space.
We accomplish this by inducing a symbiotic relationship between the learned embedding and a generative adversarial
network. This is in contrast to methods which use the adversarial framework for realistic data generation and retraining deep models with such data. We demonstrate the
strength and generality of our approach by performing experiments on three different tasks with varying levels of dif-
ficulty: (1) Digit classification (MNIST, SVHN and USPS
datasets) (2) Object recognition using OFFICE dataset and
(3) Domain adaptation from synthetic to real data. Our
method achieves state-of-the art performance in most experimental settings and by far the only GAN-based method
that has been shown to work well across different datasets
such as OFFICE and DIGITS