1 research outputs found
Using Latent Codes for Class Imbalance Problem in Unsupervised Domain Adaptation
We address the problem of severe class imbalance in unsupervised domain
adaptation, when the class spaces in source and target domains diverge
considerably. Till recently, domain adaptation methods assumed the aligned
class spaces, such that reducing distribution divergence makes the transfer
between domains easier. Such an alignment assumption is invalidated in real
world scenarios where some source classes are often under-represented or simply
absent in the target domain. We revise the current approaches to class
imbalance and propose a new one that uses latent codes in the adversarial
domain adaptation framework. We show how the latent codes can be used to
disentangle the silent structure of the target domain and to identify
under-represented classes. We show how to learn the latent code reconstruction
jointly with the domain invariant representation and use them to accurately
estimate the target labels