  Adversarial learning has been embedded into deep networks to learn
disentangled and transferable representations for domain adaptation. Existing
adversarial domain adaptation methods may not effectively align different
domains of multimodal distributions native in classification problems. In this
paper, we present conditional adversarial domain adaptation, a principled
framework that conditions the adversarial adaptation models on discriminative
information conveyed in the classifier predictions. Conditional domain
adversarial networks (CDANs) are designed with two novel conditioning
strategies: multilinear conditioning that captures the cross-covariance between
feature representations and classifier predictions to improve the
discriminability, and entropy conditioning that controls the uncertainty of
classifier predictions to guarantee the transferability. With theoretical
guarantees and a few lines of codes, the approach has exceeded state-of-the-art
results on five datasets.
