We introduce a new neural network learning algorithm suited to the context of domain adaptation, in which data at training and test time come from similar but different distributions. Our algorithm is inspired by theory on domain adaptation suggesting that, for effective domain transfer to be achieved, predictions must be made based on a data representation that cannot discriminate between the training (source) and test (target) domains. We propose a training objective that implements this idea in the context of a neural network, whose hidden layer is trained to be predictive of the classification target, but uninformative as to the domain of the input. Our experiments on a sentiment analysis classification benchmark, where the target data available at the training time is unlabeled, show that our neural network for domain adaption algorithm has better performance than either a standard neural networks and a SVM, trained on input features extracted with the state-of-the-art marginalized stacked denoising autoencoders of Chen et al. (2012).

本研究提出了一种针对领域自适应问题的新型表示学习算法, 其中训练和测试数据来自类似但不同的分布, 实验结果表明，我们的神经网络算法对领域适应具有更好的性能表现，而不管是标准神经网络还是支持向量机，即使是使用 Chen 等人提出的最先进的边缘化堆栈去噪自编码器的输入特征提取。

领域对抗神经网络