This repository provides an official implementation of a research paper on semi-supervised semantic segmentation. The method addresses the problem of underutilized pixels in unlabeled images by assigning pseudo-labels to both reliable and unreliable predictions, expanding the training data effectively. It uses deep learning with PyTorch and includes benchmarks on Cityscapes and PASCAL VOC datasets.