ContraCluster: Learning to Classify without Labels by Contrastive
Self-Supervision and Prototype-Based Semi-Supervision
- URL: http://arxiv.org/abs/2304.09369v1
- Date: Wed, 19 Apr 2023 01:51:08 GMT
- Title: ContraCluster: Learning to Classify without Labels by Contrastive
Self-Supervision and Prototype-Based Semi-Supervision
- Authors: Seongho Joe, Byoungjip Kim, Hoyoung Kang, Kyoungwon Park, Bogun Kim,
Jaeseon Park, Joonseok Lee, Youngjune Gwon
- Abstract summary: We propose ContraCluster, an unsupervised image classification method that combines clustering with the power of contrastive self-supervised learning.
ContraCluster consists of three stages: (1) contrastive self-supervised pre-training (CPT), (2) contrastive prototype sampling (CPS), and (3) prototype-based semi-supervised fine-tuning (PB-SFT).
We demonstrate empirically that ContraCluster achieves new state-of-the-art results for standard benchmark datasets including CIFAR-10, STL-10, and ImageNet-10.
- Score: 7.819942809508631
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The recent advances in representation learning inspire us to take on the
challenging problem of unsupervised image classification tasks in a principled
way. We propose ContraCluster, an unsupervised image classification method that
combines clustering with the power of contrastive self-supervised learning.
ContraCluster consists of three stages: (1) contrastive self-supervised
pre-training (CPT), (2) contrastive prototype sampling (CPS), and (3)
prototype-based semi-supervised fine-tuning (PB-SFT). CPS can select highly
accurate, categorically prototypical images in an embedding space learned by
contrastive learning. We use sampled prototypes as noisy labeled data to
perform semi-supervised fine-tuning (PB-SFT), leveraging small prototypes and
large unlabeled data to further enhance the accuracy. We demonstrate
empirically that ContraCluster achieves new state-of-the-art results for
standard benchmark datasets including CIFAR-10, STL-10, and ImageNet-10. For
example, ContraCluster achieves about 90.8% accuracy for CIFAR-10, which
outperforms DAC (52.2%), IIC (61.7%), and SCAN (87.6%) by a large margin.
Without any labels, ContraCluster can achieve a 90.8% accuracy that is
comparable to 95.8% by the best supervised counterpart.
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