We develop a set of methods to improve on the results of self-supervised
learning using context. We start with a baseline of patch based arrangement
context learning and go from there. Our methods address some overt problems
such as chromatic aberration as well as other potential problems such as
spatial skew and mid-level feature neglect. We prevent problems with testing
generalization on common self-supervised benchmark tests by using different
datasets during our development. The results of our methods combined yield top
scores on all standard self-supervised benchmarks, including classification and
detection on PASCAL VOC 2007, segmentation on PASCAL VOC 2012, and "linear
tests" on the ImageNet and CSAIL Places datasets. We obtain an improvement over
our baseline method of between 4.0 to 7.1 percentage points on transfer
learning classification tests. We also show results on different standard
network architectures to demonstrate generalization as well as portability. All
data, models and programs are available at:
this https URL

本研究采用了一种基于 patch 排列上下文学习的自监督学习方法，着重解决了光学畸变、空间倾斜和中等特征忽视等问题，在不同数据集上进行多项自监督测试，结果表明，在 PASCAL VOC 2007 的分类和检测、PASCAL VOC 2012 的分割以及 ImageNet 和 CSAIL Places 数据集上的线性测试中都取得了顶尖成绩，在迁移学习分类测试中，超过基准方法的改进幅度为 4.0 到 7.1 个百分点，同时演示了不同标准网络架构的结果，以证明其推广性和灵活性。