  Learning deep neural networks that are generalizable across different domains
remains a challenge due to the problem of domain shift. Unsupervised domain
adaptation is a promising avenue which transfers knowledge from a source domain
to a target domain without using any labels in the target domain. Contemporary
techniques focus on extracting domain-invariant features using domain
adversarial training. However, these techniques neglect to learn discriminative
class boundaries in the latent representation space on a target domain and
yield limited adaptation performance. To address this problem, we propose
distance metric guided feature alignment (MetFA) to extract discriminative as
well as domain-invariant features on both source and target domains. The
proposed MetFA method explicitly and directly learns the latent representation
without using domain adversarial training. Our model integrates class
distribution alignment to transfer semantic knowledge from a source domain to a
target domain. We evaluate the proposed method on fetal ultrasound datasets for
cross-device image classification. Experimental results demonstrate that the
proposed method outperforms the state-of-the-art and enables model
generalization.
