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Unsupervised domain adaptation (UDA) methods can dramatically improvegeneralization on unlabeled target domains. However, optimal hyper-parameterselection is critical to achieving high accuracy and avoiding negativetransfer. Supervised hyper-parameter validation is not possible without labeledtarget data, which raises the question: How can we validate unsupervisedadaptation techniques in a realistic way? We first empirically analyze existingcriteria and demonstrate that they are not very effective for tuninghyper-parameters. Intuitively, a well-trained source classifier should embedtarget samples of the same class nearby, forming dense neighborhoods in featurespace. Based on this assumption, we propose a novel unsupervised validationcriterion that measures the density of soft neighborhoods by computing theentropy of the similarity distribution between points. Our criterion is simplerthan competing validation methods, yet more effective; it can tunehyper-parameters and the number of training iterations in both imageclassification and semantic segmentation models. The code used for the paperwill be available at \url{https://github.com/VisionLearningGroup/SND}.

Tune it the Right Way: Unsupervised Validation of Domain Adaptation via Soft Neighborhood Density