Papers › Prototypical Contrastive Learning of Unsupervised Representations
Prototypical Contrastive Learning of Unsupervised Representations
Junnan Li, Pan Zhou, Caiming Xiong, Steven C. H. Hoi
This paper presents Prototypical Contrastive Learning (PCL), an unsupervised representation learning method that addresses the fundamental limitations of instance-wise contrastive learning. PCL not only learns low-level features for the task of instance discrimination, but more importantly, it implicitly encodes semantic structures of the data into the learned embedding space. Specifically, we introduce prototypes as latent variables to help find the maximum-likelihood estimation of the network parameters in an Expectation-Maximization framework. We iteratively perform E-step as finding the distribution of prototypes via clustering and M-step as optimizing the network via contrastive learning. We propose ProtoNCE loss, a generalized version of the InfoNCE loss for contrastive learning, which encourages representations to be closer to their assigned prototypes. PCL outperforms state-of-the-art instance-wise contrastive learning methods on multiple benchmarks with substantial improvement in low-resource transfer learning. Code and pretrained models are available at https://github.com/salesforce/PCL.
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Code
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Code Syntology ran Syntology
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Contrastive Learning | imagenet-1k | ResNet50 (v2) | ImageNet Top-1 Accuracy | 67.6 | #5 of 14 | Archive leaderboard | report |
| Contrastive Learning | imagenet-1k | ResNet50 | ImageNet Top-1 Accuracy | 61.5 | #8 of 14 | Archive leaderboard | report |
| Self-Supervised Image Classification | ImageNet | PCL (ResNet-50) | Number of Params | 25M | #111 of 144 | Archive leaderboard | report |
| Self-Supervised Image Classification | ImageNet | PCL (ResNet-50) | Top 1 Accuracy | 65.9% | #111 of 144 | Archive leaderboard | report |
| Semi-Supervised Image Classification | ImageNet - 1% labeled data | PCL (ResNet-50) | Top 5 Accuracy | 75.6% | #55 of 65 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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