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Link to original content: http://github.com/topics/nnclr
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nnclr

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mwritescode / nnclr-cifar100

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We implement NNCLR and a novel clustering-based technique for contrastive learning that we call KMCLR. We show that applying a clustering technique to obtain prototype embeddings and using these prototypes to form positive pairs for contrastive loss can achieve performances on par with NNCLR on CIFAR-100 while storing 0.4% of the number of vectors.

clustering k-means cifar-100 self-supervised-learning contrastive-learning nnclr
  • Updated Feb 8, 2023
  • Python

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