  Self-supervised pretraining has been shown to yield powerful representations
for transfer learning. These performance gains come at a large computational
cost however, with state-of-the-art methods requiring an order of magnitude
more computation than supervised pretraining. We tackle this computational
bottleneck by introducing a new self-supervised objective, contrastive
detection, which tasks representations with identifying object-level features
across augmentations. This objective extracts a rich learning signal per image,
leading to state-of-the-art transfer accuracy on a variety of downstream tasks,
while requiring up to 10x less pretraining. In particular, our strongest
ImageNet-pretrained model performs on par with SEER, one of the largest
self-supervised systems to date, which uses 1000x more pretraining data.
Finally, our objective seamlessly handles pretraining on more complex images
such as those in COCO, closing the gap with supervised transfer learning from
COCO to PASCAL.
