Data distillation makes omni-supervised learning possible

What this post added

Introduced data distillation, a method for omni-supervised learning that combines supervised and unsupervised data. The process involves training a model on supervised data, applying it to unsupervised data to generate labels, and then retraining the model with both datasets. This approach demonstrated the potential to surpass state-of-the-art fully supervised methods.