Data Distillation: Towards Omni-Supervised Learning Data Distillation: Towards Omni-Supervised Learning
Paper summary * It's a semi-supervised method (the goal is to make use of unlabeled data in addition to labeled data). * They first train a neural net normally, in the supervised way, on a labeled dataset. * Then **they retrain the net using *its own predictions* on the originally unlabeled data as if it was ground truth** (but only when the net is confident enough about the prediction). * More precisely they retrain on the union of the original dataset and the examples labeled by the net itself. (Each minibatch is on average 60% original and 40% self-labeled) * When making these predictions (that will subsequently used for training), they use **multi-transform inference**. * They apply the net to differently transformed versions of the image (mirroring, scaling), transform the outputs back accordingly and combine the results.
Data Distillation: Towards Omni-Supervised Learning
Radosavovic, Ilija and Dollár, Piotr and Girshick, Ross B. and Gkioxari, Georgia and He, Kaiming
arXiv e-Print archive - 2017 via Local Bibsonomy
Keywords: dblp

Summary by isarandi 3 years ago
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