  Synthetic images are one of the most promising solutions to avoid high costs
associated with generating annotated datasets to train supervised convolutional
neural networks (CNN). However, to allow networks to generalize knowledge from
synthetic to real images, domain adaptation methods are necessary. This paper
implements unsupervised domain adaptation (UDA) methods on an anchorless object
detector. Given their good performance, anchorless detectors are increasingly
attracting attention in the field of object detection. While their results are
comparable to the well-established anchor-based methods, anchorless detectors
are considerably faster. In our work, we use CenterNet, one of the most recent
anchorless architectures, for a domain adaptation problem involving synthetic
images. Taking advantage of the architecture of anchorless detectors, we
propose to adjust two UDA methods, viz., entropy minimization and maximum
squares loss, originally developed for segmentation, to object detection. Our
results show that the proposed UDA methods can increase the mAPfrom61 %to69
%with respect to direct transfer on the considered anchorless detector. The
code is available: https://github.com/scheckmedia/centernet-uda.
