  Domain adaptation addresses the problem created when training data is
generated by a so-called source distribution, but test data is generated by a
significantly different target distribution. In this work, we present
approximate label matching (ALM), a new unsupervised domain adaptation
technique that creates and leverages a rough labeling on the test samples, then
uses these noisy labels to learn a transformation that aligns the source and
target samples. We show that the transformation estimated by ALM has favorable
properties compared to transformations estimated by other methods, which do not
use any kind of target labeling. Our model is regularized by requiring that a
classifier trained to discriminate source from transformed target samples
cannot distinguish between the two. We experiment with ALM on simulated and
real data, and show that it outperforms techniques commonly used in the field.
