Robust Unsupervised Domain Adaptation for Neural Networks via Moment Alignment
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A novel approach for unsupervised domain adaptation for neural networks is proposed that relies on a metric-based regularization of the learning process. The metric-based regularization aims at domain-invariant latent feature representations by means of maximizing the similarity between domain-specific activation distributions. The proposed metric results from modifying an integral probability metric in a way such that it becomes translation-invariant on a polynomial reproducing kernel Hilbert space. The metric has an intuitive interpretation in the dual space as sum of differences of central moments of the corresponding activation distributions. As demonstrated by an analysis on standard benchmark datasets for sentiment analysis and object recognition the outlined approach shows more robustness parameter changes than state-of-the-art approaches while achieving even higher classification accuracies.
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