Adversarial Alignment of Class Prediction Uncertainties for Domain Adaptation

Understand

We consider unsupervised domain adaptation: given labelled examples from a source domain and unlabelled examples from a related target domain, the goal is to infer the labels of target examples.

• Under the assumption that features from pre-trained deep neural networks are transferable across related domains, domain adaptation reduces to aligning source and target domain at class prediction uncertainty level.
• We tackle this problem by introducing a method based on adversarial learning which forces the label uncertainty predictions on the target domain to be indistinguishable from those on the source domain.
• Pre-trained deep neural networks are used to generate deep features having high transferability across related domains.