Utilizing dataset affinity prediction in object detection to assess
  training data
Stefan Becker Jens Bayer Ronny Hug Wolfgang Hübner Michael Arens
Data pooling offers various advantages, such as increasing the sample size,
improving generalization, reducing sampling bias, and addressing data sparsity
and quality, but it is not straightforward and may even be counterproductive.
Assessing the effectiveness of pooling datasets in a principled manner is
challenging due to the difficulty in estimating the overall information content
of individual datasets. Towards this end, we propose incorporating a data
source prediction module into standard object detection pipelines. The module
runs with minimal overhead during inference time, providing additional
information about the data source assigned to individual detections. We show
the benefits of the so-called dataset affinity score by automatically selecting
samples from a heterogeneous pool of vehicle datasets. The results show that
object detectors can be trained on a significantly sparser set of training
samples without losing detection accuracy.