  In this work we address the problem of transferring knowledge obtained from a
vast annotated source domain to a low labeled target domain. We propose
Adversarial Variational Domain Adaptation (AVDA), a semi-supervised domain
adaptation method based on deep variational embedded representations. We use
approximate inference and domain adversarial methods to map samples from source
and target domains into an aligned class-dependent embedding defined as a
Gaussian Mixture Model. AVDA works as a classifier and considers a generative
model that helps this classification. We used digits dataset for
experimentation. Our results show that on a semi-supervised few-shot scenario
our model outperforms previous methods in most of the adaptation tasks, even
using a fewer number of labeled samples per class on target domain.
