Abstract
We investigate omni-supervised learning, a special regime of semi-supervised
learning in which the learner exploits all available labeled data plus
internet-scale sources of unlabeled data. Omni-supervised learning is
lower-bounded by performance on existing labeled datasets, offering the
potential to surpass state-of-the-art fully supervised methods. To exploit the
omni-supervised setting, we propose data distillation, a method that ensembles
predictions from multiple transformations of unlabeled data, using a single
model, to automatically generate new training annotations. We argue that visual
recognition models have recently become accurate enough that it is now possible
to apply classic ideas about self-training to challenging real-world data. Our
experimental results show that in the cases of human keypoint detection and
general object detection, state-of-the-art models trained with data
distillation surpass the performance of using labeled data from the COCO
dataset alone.
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