  Adapting a model to perform well on unforeseen data outside its training set
is a common problem that continues to motivate new approaches. We demonstrate
that application of batch normalization in the output layer, prior to softmax
activation, results in improved generalization across visual data domains in a
refined ResNet model. The approach adds negligible computational complexity yet
outperforms many domain adaptation methods that explicitly learn to align data
domains. We benchmark this technique on the Office-Home dataset and show that
batch normalization is competitive with other leading methods. We show that
this method is not sensitive to presence of source data during adaptation and
further present the impact on trained tensor distributions tends toward
sparsity. Code is available at https://github.com/matthewbehrend/BNC
