  Deep co-training has been introduced to semi-supervised segmentation and
achieves impressive results, yet few studies have explored the working
mechanism behind it. In this work, we revisit the core assumption that supports
co-training: multiple compatible and conditionally independent views. By
theoretically deriving the generalization upper bound, we prove the prediction
similarity between two models negatively impacts the model's generalization
ability. However, most current co-training models are tightly coupled together
and violate this assumption. Such coupling leads to the homogenization of
networks and confirmation bias which consequently limits the performance. To
this end, we explore different dimensions of co-training and systematically
increase the diversity from the aspects of input domains, different
augmentations and model architectures to counteract homogenization. Our Diverse
Co-training outperforms the state-of-the-art (SOTA) methods by a large margin
across different evaluation protocols on the Pascal and Cityscapes. For
example. we achieve the best mIoU of 76.2%, 77.7% and 80.2% on Pascal with only
92, 183 and 366 labeled images, surpassing the previous best results by more
than 5%.
