Semi-supervised learning is especially interesting in the dense prediction context due to high cost of pixel-level ground truth. Unfortunately, most such approaches are evaluated on outdated architectures which hamper research due to very slow training and high requirements on GPU RAM. We address this concern by presenting a simple and effective baseline which works very well both on standard and efficient architectures. Our baseline is based on one-way consistency and non-linear geometric and photometric perturbations. We show advantage of perturbing only the student branch and present a plausible explanation of such behaviour. Experiments on Cityscapes and CIFAR-10 demonstrate competitive performance with respect to prior work.

翻译：由于像素级地面真相成本高昂,半监督的学习在密集的预测环境中尤其令人感兴趣,不幸的是,大多数这类方法都是在过时的建筑上评价的,这些建筑由于培训非常缓慢和对GPU RAM的要求很高而阻碍了研究。我们通过提出一个简单而有效的基准来解决这一关切,该基准在标准结构和效率高的建筑上都非常有效。我们的基线以单向一致性和非线性几何和光度干扰为基础。我们显示出只有学生分支才受到干扰的优势,并对这种行为提出可信的解释。城市景景和CIFAR-10实验展示了以前工作的竞争性表现。