  We investigate the generalization of semi-supervised learning (SSL) to
diverse pixel-wise tasks. Although SSL methods have achieved impressive results
in image classification, the performances of applying them to pixel-wise tasks
are unsatisfactory due to their need for dense outputs. In addition, existing
pixel-wise SSL approaches are only suitable for certain tasks as they usually
require to use task-specific properties. In this paper, we present a new SSL
framework, named Guided Collaborative Training (GCT), for pixel-wise tasks,
with two main technical contributions. First, GCT addresses the issues caused
by the dense outputs through a novel flaw detector. Second, the modules in GCT
learn from unlabeled data collaboratively through two newly proposed
constraints that are independent of task-specific properties. As a result, GCT
can be applied to a wide range of pixel-wise tasks without structural
adaptation. Our extensive experiments on four challenging vision tasks,
including semantic segmentation, real image denoising, portrait image matting,
and night image enhancement, show that GCT outperforms state-of-the-art SSL
methods by a large margin. Our code available at:
https://github.com/ZHKKKe/PixelSSL.
