Self-Supervised Learning by Estimating Twin Class Distribution

Codes and pretrained models for TWIST:

@article{wang2021self,
  title={Self-Supervised Learning by Estimating Twin Class Distributions},
  author={Wang, Feng and Kong, Tao and Zhang, Rufeng and Liu, Huaping and Li, Hang},
  journal={arXiv preprint arXiv:2110.07402},
  year={2021}
}

TWIST is a novel self-supervised representation learning method by classifying large-scale unlabeled datasets in an end-to-end way. We employ a siamese network terminated by a softmax operation to produce twin class distributions of two augmented images. Without supervision, we enforce the class distributions of different augmentations to

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