In this paper we study the problem of image representation learning without
human annotation. By following the principles of self-supervision, we build a
convolutional neural network (CNN) that can be trained to solve Jigsaw puzzles
as a pretext task, which requires no manual labeling, and then later repurposed
to solve object classification and detection. To maintain the compatibility
across tasks we introduce the context-free network (CFN), a siamese-ennead CNN.
The CFN takes image tiles as input and explicitly limits the receptive field
(or context) of its early processing units to one tile at a time. We show that
the CFN includes fewer parameters than AlexNet while preserving the same
semantic learning capabilities. By training the CFN to solve Jigsaw puzzles, we
learn both a feature mapping of object parts as well as their correct spatial
arrangement. Our experimental evaluations show that the learned features
capture semantically relevant content. Our proposed method for learning visual
representations outperforms state of the art methods in several transfer
learning benchmarks.

本文研究了无需人工注释的图像表示学习问题，通过自监督学习的方式，构建了一个卷积神经网络，该网络能够以拼图问题作为预文本任务进行训练，从而无需手动标注，并在后续用于解决目标分类和检测问题；同时引入了一种名为 CFN 的无上下文网络，该网络以图像块作为输入，并将其早期处理单元的感受野（或上下文）明确限制为一次一个图像块，以保持跨任务的兼容性。通过训练 CFN 来解决拼图问题，我们学习了对象部分的特征映射以及它们的正确空间排列，实验评估结果表明，所学习的特征捕获了语义相关的内容；本文提出的学习视觉表示的方法在多个转移学习基准测试中胜过现有的最先进方法。