For many applications the collection of labeled data is expensive laborious. Exploitation of unlabeled data during training is thus a long pursued objective of machine learning. Self-supervised learning addresses this by positing an auxiliary task (different, but related to the supervised task) for which data is abundantly available. In this paper, we show how ranking can be used as a proxy task for some regression problems. As another contribution, we propose an efficient backpropagation technique for Siamese networks which prevents the redundant computation introduced by the multi-branch network architecture. We apply our framework to two regression problems: Image Quality Assessment (IQA) and Crowd Counting. For both we show how to automatically generate ranked image sets from unlabeled data. Our results show that networks trained to regress to the ground truth targets for labeled data and to simultaneously learn to rank unlabeled data obtain significantly better, state-of-the-art results for both IQA and crowd counting. In addition, we show that measuring network uncertainty on the self-supervised proxy task is a good measure of informativeness of unlabeled data. This can be used to drive an algorithm for active learning and we show that this reduces labeling effort by up to 50%.

本文介绍了一种使用排序作为代理任务的自监督学习方法，以及一种有效的后向传播技术，使用该方法可以在标记数据和无标记数据上同时进行回归任务和排序任务的训练，结果表明使用自监督学习可以在图像质量评估（IQA）和人群计数方面实现更好的结果，并且利用无标记数据的信息量衡量方法可以使半监督学习方法的标记代价降低50％。

自监督学习在CNN中的无标签数据利用