What is the difference between BatchNorm and Adaptive BatchNorm (AdaBN)? I understand that BatchNorm (Batch Normalization) centers to (mean, std) = (0, 1) and potentially scales (with $ \gamma $) and offsets (with $ \beta $) the data which is input to the layer. BatchNorm follows this formula:


(retrieved from arxiv-id 1502.03167)

However, when it comes to 'adaptive BatchNorm', I don't understand what the difference is. What is adaptive BatchNorm doing differently? It is described as follows:


(retrieved from arxiv-id 1603.04779)
I think the original batch normalization paper proposes to use mean and standard deviation estimated on the train set. The adaptive batch normalization simply re-estimates them on the target domain (could be the test set, or some unlabeled data from the target domain). 

Please correct me if I am wrong.