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CycleCluster: Modernising Clustering Regularisation for Deep Semi-Supervised Classification
[article]
2021
arXiv
pre-print
Given the potential difficulties in obtaining large quantities of labelled data, many works have explored the use of deep semi-supervised learning, which uses both labelled and unlabelled data to train a neural network architecture. The vast majority of SSL approaches focus on implementing the low-density separation assumption or consistency assumption, the idea that decision boundaries should lie in low density regions. However, they have implemented this assumption by making local changes to
arXiv:2001.05317v2
fatcat:ispbyuphojgxtfvyzolzpxki74