VISER: Visual Self-Regularization

7 Feb 2018 Hamid Izadinia Pierre Garrigues

In this work, we propose the use of large set of unlabeled images as a source of regularization data for learning robust visual representation. Given a visual model trained by a labeled dataset in a supervised fashion, we augment our training samples by incorporating large number of unlabeled data and train a semi-supervised model... (read more)

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