Fix-A-Step: Effective Semi-supervised Learning from Uncurated Unlabeled Sets

@article{Huang2022FixAStepES,
  title={Fix-A-Step: Effective Semi-supervised Learning from Uncurated Unlabeled Sets},
  author={Zhe Huang and Mary-Joy Sidhom and Benjamin S. Wessler and Michael C. Hughes},
  journal={ArXiv},
  year={2022},
  volume={abs/2208.11870}
}
Semi-supervised learning (SSL) promises gains in accuracy compared to training classifiers on small labeled datasets by also training on many unlabeled images. In realistic applica- tions like medical imaging, unlabeled sets will be collected for expediency and thus uncurated : possibly different from the labeled set in represented classes or class frequencies. Un-fortunately, modern deep SSL often makes accuracy worse when given uncurated unlabeled sets. Recent remedies suggest filtering… 
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