Abstract
We propose a general framework for unsupervised
domain adaptation, which allows deep neural networks
trained on a source domain to be tested on a different target domain without requiring any training annotations in
the target domain. This is achieved by adding extra networks and losses that help regularize the features extracted
by the backbone encoder network. To this end we propose
the novel use of the recently proposed unpaired image-toimage translation framework to constrain the features extracted by the encoder network. Specifically, we require that
the features extracted are able to reconstruct the images in
both domains. In addition we require that the distribution
of features extracted from images in the two domains are
indistinguishable. Many recent works can be seen as specific cases of our general framework. We apply our method
for domain adaptation between MNIST, USPS, and SVHN
datasets, and Amazon, Webcam and DSLR Office datasets in
classification tasks, and also between GTA5 and Cityscapes
datasets for a segmentation task. We demonstrate state of
the art performance on each of these datasets