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Domain adaptation provides a powerful set of model training techniques givendomain-specific training data and supplemental data with unknown relevance. Thetechniques are useful when users need to develop models with data from varyingsources, of varying quality, or from different time ranges. We buildCrossTrainer, a system for practical domain adaptation. CrossTrainer utilizesloss reweighting, which provides consistently high model accuracy across avariety of datasets in our empirical analysis. However, loss reweighting issensitive to the choice of a weight hyperparameter that is expensive to tune.We develop optimizations leveraging unique properties of loss reweighting thatallow CrossTrainer to output accurate models while improving training timecompared to naive hyperparameter search.

CrossTrainer: Practical Domain Adaptation with Loss Reweighting