  This work addresses the problem of semi-supervised image classification tasks
with the integration of several effective self-supervised pretext tasks.
Different from widely-used consistency regularization within semi-supervised
learning, we explored a novel self-supervised semi-supervised learning
framework (Color-$S^{4}L$) especially with image colorization proxy task and
deeply evaluate performances of various network architectures in such special
pipeline. Also, we demonstrated its effectiveness and optimal performance on
CIFAR-10, SVHN and CIFAR-100 datasets in comparison to previous supervised and
semi-supervised optimal methods.
