  Unsupervised domain adaptation (UDA) methods can dramatically improve
generalization on unlabeled target domains. However, optimal hyper-parameter
selection is critical to achieving high accuracy and avoiding negative
transfer. Supervised hyper-parameter validation is not possible without labeled
target data, which raises the question: How can we validate unsupervised
adaptation techniques in a realistic way? We first empirically analyze existing
criteria and demonstrate that they are not very effective for tuning
hyper-parameters. Intuitively, a well-trained source classifier should embed
target samples of the same class nearby, forming dense neighborhoods in feature
space. Based on this assumption, we propose a novel unsupervised validation
criterion that measures the density of soft neighborhoods by computing the
entropy of the similarity distribution between points. Our criterion is simpler
than competing validation methods, yet more effective; it can tune
hyper-parameters and the number of training iterations in both image
classification and semantic segmentation models. The code used for the paper
will be available at \url{https://github.com/VisionLearningGroup/SND}.
