Directional Self-supervised Learning for Heavy Image Augmentations

@article{Bai2021DirectionalSL,
  title={Directional Self-supervised Learning for Heavy Image Augmentations},
  author={Yalong Bai and Yifan Yang and Wei Zhang and Tao Mei},
  journal={2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2021},
  pages={16671-16680}
}
  • Yalong BaiYifan Yang Tao Mei
  • Published 26 October 2021
  • Computer Science
  • 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Despite the large augmentation family, only a few cherry-picked robust augmentation policies are beneficial to self-supervised image representation learning. In this paper, we propose a directional self-supervised learning paradigm (DSSL), which is compatible with significantly more augmentations. Specifically, we adapt heavy augmentation policies after the views lightly augmented by standard augmentations, to generate harder view (HV). HV usually has a higher deviation from the original image… 
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