Data distillation makes omni-supervised learning possible

This post introduces and explores 'data distillation' as a method for omni-supervised learning. It details a four-step process: training on supervised data, applying to unsupervised data, generating labels for unsupervised data, and retraining with both. The experiments demonstrate that omni-supervised learning can surpass fully supervised learning, suggesting that combining curated and unlabeled data is a viable strategy for accelerating AI system creation.