  Pre-training is a dominant paradigm in computer vision. For example,
supervised ImageNet pre-training is commonly used to initialize the backbones
of object detection and segmentation models. He et al., however, show a
surprising result that ImageNet pre-training has limited impact on COCO object
detection. Here we investigate self-training as another method to utilize
additional data on the same setup and contrast it against ImageNet
pre-training. Our study reveals the generality and flexibility of self-training
with three additional insights: 1) stronger data augmentation and more labeled
data further diminish the value of pre-training, 2) unlike pre-training,
self-training is always helpful when using stronger data augmentation, in both
low-data and high-data regimes, and 3) in the case that pre-training is
helpful, self-training improves upon pre-training. For example, on the COCO
object detection dataset, pre-training benefits when we use one fifth of the
labeled data, and hurts accuracy when we use all labeled data. Self-training,
on the other hand, shows positive improvements from +1.3 to +3.4AP across all
dataset sizes. In other words, self-training works well exactly on the same
setup that pre-training does not work (using ImageNet to help COCO). On the
PASCAL segmentation dataset, which is a much smaller dataset than COCO, though
pre-training does help significantly, self-training improves upon the
pre-trained model. On COCO object detection, we achieve 54.3AP, an improvement
of +1.5AP over the strongest SpineNet model. On PASCAL segmentation, we achieve
90.5 mIOU, an improvement of +1.5% mIOU over the previous state-of-the-art
result by DeepLabv3+.
