State-of-the-art methods for unsupervised representation learning can train well the first few layers of standard convolutional neural networks, but they are not as good as supervised learning for deeper layers. This is likely due to the generic and relatively simple nature of shallow layers; and yet, these approaches are applied to millions of images, scalability being advertised as their major advantage since unlabelled data is cheap to collect. In this paper we question this practice and ask whether so many images are actually needed to learn the layers for which unsupervised learning works best. Our main result is that a few or even a single image together with strong data augmentation are sufficient to nearly saturate performance. Specifically, we provide an analysis for three different self-supervised feature learning methods (BiGAN, RotNet, DeepCluster) vs number of training images (1, 10, 1000) and show that we can top the accuracy for the first two convolutional layers of common networks using just a single unlabelled training image and obtain competitive results for other layers. We further study and visualize the learned representation as a function of which (single) image is used for training. Our results are also suggestive of which type of information may be captured by shallow layers in deep networks.

本文对于深度卷积神经网络的自监督技术进行了批判性的探讨，指出在使用强大的数据增强技术后，三种不同但具有代表性的方法可以从单个图像中学习卷积网络的前几层，但是对于更深层的神经网络，即使使用了数百万张未标记的图像来进行训练，与手动监督的差距也无法弥补。该研究得出的结论是：（1）深度网络的早期层的权值包含的自然图像的统计信息非常有限，（2）这种低层次的统计信息可以通过自监督学习和强监督学习一样学习，（3）可以通过使用合成变换来捕获低层次的统计信息，而无需使用大量的图像数据集。

自监督关键分析：或从单张图像中获得的经验教训