Data Distillation: Towards Omni-Supervised Learning

TL;DR

This paper introduces data distillation, a method for omni-supervised learning that leverages all available labeled data and unlabeled internet-scale data to improve visual recognition models beyond traditional supervised methods.

Contribution

The paper proposes data distillation, a novel ensemble-based approach for utilizing unlabeled data in omni-supervised learning, demonstrating improved performance in visual recognition tasks.

Findings

Data distillation surpasses COCO supervised performance in human keypoint detection.

It also improves object detection accuracy beyond labeled-only training.

The method effectively leverages unlabeled data for state-of-the-art results.

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.