  Semi-supervised learning is a challenging problem which aims to construct a
model by learning from limited labeled examples. Numerous methods for this task
focus on utilizing the predictions of unlabeled instances consistency alone to
regularize networks. However, treating labeled and unlabeled data separately
often leads to the discarding of mass prior knowledge learned from the labeled
examples. %, and failure to mine the feature interaction between the labeled
and unlabeled image pairs. In this paper, we propose a novel method for
semi-supervised semantic segmentation named GuidedMix-Net, by leveraging
labeled information to guide the learning of unlabeled instances. Specifically,
GuidedMix-Net employs three operations: 1) interpolation of similar
labeled-unlabeled image pairs; 2) transfer of mutual information; 3)
generalization of pseudo masks. It enables segmentation models can learning the
higher-quality pseudo masks of unlabeled data by transfer the knowledge from
labeled samples to unlabeled data. Along with supervised learning for labeled
data, the prediction of unlabeled data is jointly learned with the generated
pseudo masks from the mixed data. Extensive experiments on PASCAL VOC 2012, and
Cityscapes demonstrate the effectiveness of our GuidedMix-Net, which achieves
competitive segmentation accuracy and significantly improves the mIoU by +7$\%$
compared to previous approaches.
