Fast OT for Latent Domain Adaptation

The discriminator Such a shift in data distribution is seen and addressed in on the other hand, attempts to discriminate between almost every field ranging from Natural Language Processing a real data sample and that from the generator. Both models are (NLP) to Object Recognition. Given labeled samples from approximated by neural networks. When trained alternatively, a source domain, there are two groups that any Domain the generator learns to produce random samples from the data Adaptation (DA) approach can be classified into, i) semisupervised distribution which are very close to the real data samples. DA: some samples in the target domain are labeled Following this, Conditional Generative Adversarial Networks or ii) unsupervised DA: none of the samples in the target (CGANs) were proposed in [8]. These networks were trained domain are labeled.