  We investigate and characterize the inherent resilience of conditional
Generative Adversarial Networks (cGANs) against noise in their conditioning
labels, and exploit this fact in the context of Unsupervised Domain Adaptation
(UDA). In UDA, a classifier trained on the labelled source set can be used to
infer pseudo-labels on the unlabelled target set. However, this will result in
a significant amount of misclassified examples (due to the well-known domain
shift issue), which can be interpreted as noise injection in the ground-truth
labels for the target set. We show that cGANs are, to some extent, robust
against such "shift noise". Indeed, cGANs trained with noisy pseudo-labels, are
able to filter such noise and generate cleaner target samples. We exploit this
finding in an iterative procedure where a generative model and a classifier are
jointly trained: in turn, the generator allows to sample cleaner data from the
target distribution, and the classifier allows to associate better labels to
target samples, progressively refining target pseudo-labels. Results on common
benchmarks show that our method performs better or comparably with the
unsupervised domain adaptation state of the art.
