  While many real-world data streams imply that they change frequently in a
nonstationary way, most of deep learning methods optimize neural networks on
training data, and this leads to severe performance degradation when dataset
shift happens. However, it is less possible to annotate or inspect newly
streamed data by humans, and thus it is desired to measure model drift at
inference time in an unsupervised manner. In this paper, we propose a novel
method of model drift estimation by exploiting statistics of batch
normalization layer on unlabeled test data. To remedy possible sampling error
of streamed input data, we adopt low-rank approximation to each
representational layer. We show the effectiveness of our method not only on
dataset shift detection but also on model selection when there are multiple
candidate models among model zoo or training trajectories in an unsupervised
way. We further demonstrate the consistency of our method by comparing model
drift scores between different network architectures.
