GAR: An efficient and scalable Graph-based Activity Regularization for semi-supervised learning

19 May 2017Ozsel KilincIsmail Uysal

In this paper, we propose a novel graph-based approach for semi-supervised learning problems, which considers an adaptive adjacency of the examples throughout the unsupervised portion of the training. Adjacency of the examples is inferred using the predictions of a neural network model which is first initialized by a supervised pretraining... (read more)

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