Improvements to context based self-supervised learning
This work addresses incremental improvements in self-supervised learning for computer vision tasks, benefiting researchers and practitioners in the field.
The paper tackles problems in context-based self-supervised learning, such as chromatic aberration and spatial skew, and achieves top scores on standard benchmarks like PASCAL VOC and ImageNet, with improvements of 4.0 to 7.1 percentage points over the baseline in transfer learning classification tests.
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: https://gdo-datasci.llnl.gov/selfsupervised/.