Data Distillation: Towards Omni-Supervised Learning
This addresses the challenge of leveraging large-scale unlabeled data for visual recognition, offering potential improvements over fully supervised methods, though it appears incremental as it builds on classic self-training ideas.
The paper tackles the problem of omni-supervised learning by proposing data distillation, a method that ensembles predictions from unlabeled data to generate training annotations, resulting in state-of-the-art models for human keypoint and object detection that surpass performance using only labeled COCO data.
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.