CLS: Cross Labeling Supervision for Semi-Supervised Learning. (arXiv:2202.08502v1 [cs.CV])

It is well known that the success of deep neural networks is greatly
attributed to large-scale labeled datasets. However, it can be extremely
time-consuming and laborious to collect sufficient high-quality labeled data in
most practical applications. Semi-supervised learning (SSL) provides an
effective solution to reduce the cost of labeling by simultaneously leveraging
both labeled and unlabeled data. In this work, we present Cross Labeling
Supervision (CLS), a framework that generalizes the typical pseudo-labeling
process. Based on FixMatch, where a pseudo …

arxiv cv labeling learning semi-supervised learning supervised learning