Detector

Algorithm

Telecommunications

Classifier (UML)

Computer science

Physics

Stochastic gradient descent

Discriminator

Mathematics

Optics

Domain (mathematical analysis)

Mathematical analysis

Artificial intelligence

Domain adaptation

Artificial neural network

Machine learning

Adaptation (eye)

Adversarial system

Recent advances in deep domain adaptation reveal that adversarial learning can be embedded into deep networks to learn transferable features that reduce distribution discrepancy between the source and target domains. Existing domain adversarial adaptation methods based on single domain discriminator only align the source and target data distributions without exploiting the complex multimode structures. In this paper, we present a multi-adversarial domain adaptation (MADA) approach, which captures multimode structures to enable fine-grained alignment of different data distributions based on multiple domain discriminators. The adaptation can be achieved by stochastic gradient descent with the gradients computed by back-propagation in linear-time. Empirical evidence demonstrates that the proposed model outperforms state of the art methods on standard domain adaptation datasets.

Multi-Adversarial Domain Adaptation