Pushing the limit of semi-supervised learning with the Unified Semi-supervised Learning Benchmark - Microsoft Research
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Neural models give competitive results when trained with supervised learning using sufficient high-quality labeled data. For example, according to statistics from the Paperswithcode website, recent traditional supervised learning methods can achieve an accuracy of over 88% on the ImageNet dataset, which contains millions of data. However, acquiring large amounts of labeled data is often time-consuming […]
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Pre-Trained Language Models and Their Applications - ScienceDirect
The Quiet Semi-Supervised Revolution, by Vincent Vanhoucke
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A viable framework for semi-supervised learning on realistic dataset
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Certainty driven consistency loss on multi-teacher networks for semi-supervised learning - ScienceDirect
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Unified dual-label semi-supervised learning with top-k feature selection - ScienceDirect
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Barlow Twins: Self-Supervised Learning
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Deep learning in optical metrology: a review
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Semi-Supervised Learning in Computer Vision
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Semi-Supervised Learning in Computer Vision