  We present SSOD, the first end-to-end analysis-by synthesis framework with
controllable GANs for the task of self-supervised object detection. We use
collections of real world images without bounding box annotations to learn to
synthesize and detect objects. We leverage controllable GANs to synthesize
images with pre-defined object properties and use them to train object
detectors. We propose a tight end-to-end coupling of the synthesis and
detection networks to optimally train our system. Finally, we also propose a
method to optimally adapt SSOD to an intended target data without requiring
labels for it. For the task of car detection, on the challenging KITTI and
Cityscapes datasets, we show that SSOD outperforms the prior state-of-the-art
purely image-based self-supervised object detection method Wetectron. Even
without requiring any 3D CAD assets, it also surpasses the state-of-the-art
rendering based method Meta-Sim2. Our work advances the field of
self-supervised object detection by introducing a successful new paradigm of
using controllable GAN-based image synthesis for it and by significantly
improving the baseline accuracy of the task. We open-source our code at
https://github.com/NVlabs/SSOD.
