  Effort in releasing large-scale datasets may be compromised by privacy and
intellectual property considerations. A feasible alternative is to release
pre-trained models instead. While these models are strong on their original
task (source domain), their performance might degrade significantly when
deployed directly in a new environment (target domain), which might not contain
labels for training under realistic settings. Domain adaptation (DA) is a known
solution to the domain gap problem, but usually requires labeled source data.
In this paper, we study the problem of source free domain adaptation (SFDA),
whose distinctive feature is that the source domain only provides a pre-trained
model, but no source data. Being source free adds significant challenges to DA,
especially when considering that the target dataset is unlabeled. To solve the
SFDA problem, we propose an image translation approach that transfers the style
of target images to that of unseen source images. To this end, we align the
batch-wise feature statistics of generated images to that stored in batch
normalization layers of the pre-trained model. Compared with directly
classifying target images, higher accuracy is obtained with these style
transferred images using the pre-trained model. On several image classification
datasets, we show that the above-mentioned improvements are consistent and
statistically significant.
