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Revitalizing CNN Attention via Transformers in Self-Supervised Visual Representation Learning


This repository is the official implementation of CARE paper.
Graph

  • ResNet-50 100epoch trained on ImageNet using ResNet-50 with 100 epochs. Training log and Evaluation log
  • [ResNet-50 200epoch] trained on ImageNet using ResNet-50 with 200 epochs.
  • [ResNet-50 400epoch] trained on ImageNet using ResNet-50 with 400 epochs.

More models are provided in the following model zoo part.

📋 We will provide more pretrained models in the future.

momentum2-teacher codebase for providing helpful code.

Citation

If you think our work is useful, please feel free to cite our paper 😆 :

@inproceedings{chongjian_nips21_care,
  title={Revitalizing CNN Attentions via Transformers in Self-Supervised Visual Representation Learning},
  author={Ge, Chongjian and Liang, Youwei and Song, Yibing and Jiao, Jianbo and Wang, Jue and Luo Ping},
  booktitle="Advances in Neural Information Processing Systems",
  year={2021},
}

GitHub

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