Theoretical Analysis of Self-Training with Deep Networks on Unlabeled Data
international conference on learning representations, 2020.
This paper provides accuracy guarantees for self-training with deep networks on polynomial unlabeled samples for semi-supervised learning, unsupervised domain adaptation, and unsupervised learning.
Self-training algorithms, which train a model to fit pseudolabels predicted by another previously-learned model, have been very successful for learning with unlabeled data using neural networks. However, the current theoretical understanding of self-training only applies to linear models. This work provides a unified theoretical analysi...More
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