Abstract
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 selfsupervised 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: https://gdo-datasci.
llnl.gov/selfsupervised/.