Batch weight for domain adaptation with mass shift

Understand

Unsupervised domain transfer is the task of transferring or translating samples from a source distribution to a different target distribution.

• Current solutions unsupervised domain transfer often operate on data on which the modes of the distribution are well-matched, for instance have the same frequencies of classes between source and target distributions.
• However, these models do not perform well when the modes are not well-matched, as would be the case when samples are drawn independently from two different, but related, domains.
• This mode imbalance is problematic as generative adversarial networks (GANs), a successful approach in this setting, are sensitive to mode frequency, which results in a mismatch of semantics between source samples and generated samples of the target distribution.