Generation of Synthetic Images for Pedestrian Detection Using a Sequence
  of GANs
Viktor Seib Malte Roosen Ida Germann Stefan Wirtz Dietrich Paulus
Creating annotated datasets demands a substantial amount of manual effort. In
this proof-of-concept work, we address this issue by proposing a novel image
generation pipeline. The pipeline consists of three distinct generative
adversarial networks (previously published), combined in a novel way to augment
a dataset for pedestrian detection. Despite the fact that the generated images
are not always visually pleasant to the human eye, our detection benchmark
reveals that the results substantially surpass the baseline. The presented
proof-of-concept work was done in 2020 and is now published as a technical
report after a three years retention period.