Skip to yearly menu bar Skip to main content


Towards Accurate Model Selection in Deep Unsupervised Domain Adaptation

Kaichao You · Ximei Wang · Mingsheng Long · Michael Jordan

Pacific Ballroom #259

Keywords: [ Transfer and Multitask Learning ]


Deep unsupervised domain adaptation (Deep UDA) methods successfully leverage rich labeled data in a source domain to boost the performance on related but unlabeled data in a target domain. However, algorithm comparison is cumbersome in Deep UDA due to the absence of accurate and standardized model selection method, posing an obstacle to further advances in the field. Existing model selection methods for Deep UDA are either highly biased, restricted, unstable, or even controversial (requiring labeled target data). To this end, we propose Deep Embedded Validation (DEV), which embeds adapted feature representation into the validation procedure to obtain unbiased estimation of the target risk with bounded variance. The variance is further reduced by the technique of control variate. The efficacy of the method has been justified both theoretically and empirically.

Live content is unavailable. Log in and register to view live content