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Transformer Based Multi-Source Domain Adaptation
[article]
2020
arXiv
pre-print
In practical machine learning settings, the data on which a model must make predictions often come from a different distribution than the data it was trained on. Here, we investigate the problem of unsupervised multi-source domain adaptation, where a model is trained on labelled data from multiple source domains and must make predictions on a domain for which no labelled data has been seen. Prior work with CNNs and RNNs has demonstrated the benefit of mixture of experts, where the predictions
arXiv:2009.07806v1
fatcat:7jhd2qr63nfsrigt2guvf2fbga