MetaCL: a semi-supervised meta learning architecture via contrastive learning

This work proposes MetaCL, a meta learning architecture that uses only a traditional backbone without any priors, and introduces an unsupervised loss to minimize component redundancy and maximize variability, achieving soft-whitening and soft-alignment constraints.