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Domain-Adversarial Training of Neural Networks
[article]
2016
arXiv
pre-print
We introduce a new representation learning approach for domain adaptation, in which data at training and test time come from similar but different distributions. Our approach is directly inspired by the theory on domain adaptation suggesting that, for effective domain transfer to be achieved, predictions must be made based on features that cannot discriminate between the training (source) and test (target) domains. The approach implements this idea in the context of neural network architectures
arXiv:1505.07818v4
fatcat:xj7camuj3fagdmfgqqf2mrnmb4