Conditional Adversarial Domain Adaptation

Understand

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.