AI RESEARCH
Feature Space Renormalization for Semi-supervised Learning
arXiv CS.LG
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ArXi:2311.04055v2 Announce Type: replace Semi-supervised learning (SSL) has been proven to be a powerful method for leveraging unlabeled data to alleviate models'dependence on large labeled datasets. The common framework among recent approaches is to train the model on a large amount of unlabeled data with consistency regularization to constrain the model predictions to be invariant to input perturbation.