  Unsupervised domain adaptation techniques have been successful for a wide
range of problems where supervised labels are limited. The task is to classify
an unlabeled `target' dataset by leveraging a labeled `source' dataset that
comes from a slightly similar distribution. We propose metric-based adversarial
discriminative domain adaptation (M-ADDA) which performs two main steps. First,
it uses a metric learning approach to train the source model on the source
dataset by optimizing the triplet loss function. This results in clusters where
embeddings of the same label are close to each other and those with different
labels are far from one another. Next, it uses the adversarial approach (as
that used in ADDA \cite{2017arXiv170205464T}) to make the extracted features
from the source and target datasets indistinguishable. Simultaneously, we
optimize a novel loss function that encourages the target dataset's embeddings
to form clusters. While ADDA and M-ADDA use similar architectures, we show that
M-ADDA performs significantly better on the digits adaptation datasets of MNIST
and USPS. This suggests that using metric-learning for domain adaptation can
lead to large improvements in classification accuracy for the domain adaptation
task. The code is available at \url{https://github.com/IssamLaradji/M-ADDA}.

Machine Learning;Machine Learning