  In this paper, we propose a novel domain adaptation method for the
source-free setting. In this setting, we cannot access source data during
adaptation, while unlabeled target data and a model pretrained with source data
are given. Due to lack of source data, we cannot directly match the data
distributions between domains unlike typical domain adaptation algorithms. To
cope with this problem, we propose utilizing batch normalization statistics
stored in the pretrained model to approximate the distribution of unobserved
source data. Specifically, we fix the classifier part of the model during
adaptation and only fine-tune the remaining feature encoder part so that batch
normalization statistics of the features extracted by the encoder match those
stored in the fixed classifier. Additionally, we also maximize the mutual
information between the features and the classifier's outputs to further boost
the classification performance. Experimental results with several benchmark
datasets show that our method achieves competitive performance with
state-of-the-art domain adaptation methods even though it does not require
access to source data.
