  Unsupervised domain adaptation (UDA) assumes that source and target domain
data are freely available and usually trained together to reduce the domain
gap. However, considering the data privacy and the inefficiency of data
transmission, it is impractical in real scenarios. Hence, it draws our eyes to
optimize the network in the target domain without accessing labeled source
data. To explore this direction in object detection, for the first time, we
propose a source data-free domain adaptive object detection (SFOD) framework
via modeling it into a problem of learning with noisy labels. Generally, a
straightforward method is to leverage the pre-trained network from the source
domain to generate the pseudo labels for target domain optimization. However,
it is difficult to evaluate the quality of pseudo labels since no labels are
available in target domain. In this paper, self-entropy descent (SED) is a
metric proposed to search an appropriate confidence threshold for reliable
pseudo label generation without using any handcrafted labels. Nonetheless,
completely clean labels are still unattainable. After a thorough experimental
analysis, false negatives are found to dominate in the generated noisy labels.
Undoubtedly, false negatives mining is helpful for performance improvement, and
we ease it to false negatives simulation through data augmentation like Mosaic.
Extensive experiments conducted in four representative adaptation tasks have
demonstrated that the proposed framework can easily achieve state-of-the-art
performance. From another view, it also reminds the UDA community that the
labeled source data are not fully exploited in the existing methods.
