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Our approach is directly inspired by the theory on domain\nadaptation suggesting that, for effective domain transfer to be achieved,\npredictions must be made based on features that cannot discriminate between the\ntraining (source) and test (target) domains. The approach implements this idea\nin the context of neural network architectures that are trained on labeled data\nfrom the source domain and unlabeled data from the target domain (no labeled\ntarget-domain data is necessary). As the training progresses, the approach\npromotes the emergence of features that are (i) discriminative for the main\nlearning task on the source domain and (ii) indiscriminate with respect to the\nshift between the domains. We show that this adaptation behaviour can be\nachieved in almost any feed-forward model by augmenting it with few standard\nlayers and a new gradient reversal layer. The resulting augmented architecture\ncan be trained using standard backpropagation and stochastic gradient descent,\nand can thus be implemented with little effort using any of the deep learning\npackages. We demonstrate the success of our approach for two distinct\nclassification problems (document sentiment analysis and image classification),\nwhere state-of-the-art domain adaptation performance on standard benchmarks is\nachieved. We also validate the approach for descriptor learning task in the\ncontext of person re-identification application.","url_abs":"http://arxiv.org/abs/1505.07818v4","url_pdf":"http://arxiv.org/pdf/1505.07818v4.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 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