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.

翻译：本文针对半监督图像分类任务，通过整合多种有效的自监督代理任务来解决问题。与半监督学习中广泛采用的一致性正则化方法不同，我们探索了一种新颖的自监督半监督学习框架（Color-$S^{4}L$），特别引入图像彩色化作为代理任务，并深入评估了不同网络架构在该特殊流程中的性能表现。此外，我们在CIFAR-10、SVHN和CIFAR-100数据集上验证了该方法的有效性及最优性能，并与先前监督及半监督最优方法进行了对比。