Papers › Self-Supervised Learning for Large-Scale Unsupervised Image Clustering
Self-Supervised Learning for Large-Scale Unsupervised Image Clustering
Evgenii Zheltonozhskii, Chaim Baskin, Alex M. Bronstein, Avi Mendelson
Unsupervised learning has always been appealing to machine learning researchers and practitioners, allowing them to avoid an expensive and complicated process of labeling the data. However, unsupervised learning of complex data is challenging, and even the best approaches show much weaker performance than their supervised counterparts. Self-supervised deep learning has become a strong instrument for representation learning in computer vision. However, those methods have not been evaluated in a fully unsupervised setting. In this paper, we propose a simple scheme for unsupervised classification based on self-supervised representations. We evaluate the proposed approach with several recent self-supervised methods showing that it achieves competitive results for ImageNet classification (39% accuracy on ImageNet with 1000 clusters and 46% with overclustering). We suggest adding the unsupervised evaluation to a set of standard benchmarks for self-supervised learning. The code is available at https://github.com/Randl/kmeans_selfsuper
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Code
Syntology Ran 2 of 11 code samples harvested from 1 repository linked to this paper; 9 have no recorded run. Of those that ran: 2 ran · our draft was wrong.
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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 |
|---|---|---|---|---|---|---|---|
| Image Classification | ObjectNet | BigBiGAN (RevNet-50 4×) | Top-1 Accuracy | 4.92 | #105 of 106 | Archive leaderboard | report |
| Unsupervised Image Classification | ImageNet | SimCLRv2 ResNet-152 + SK (PCA+k-means, 1500 clusters) | ARI | 23.94±0.16 | #3 of 9 | Archive leaderboard | report |
| Unsupervised Image Classification | ImageNet | SimCLRv2 ResNet-152 + SK (PCA+k-means, 1500 clusters) | Accuracy (%) | 46.03±0.21 | #3 of 9 | Archive leaderboard | report |
| Unsupervised Image Classification | ImageNet | SimCLRv2 ResNet-152 + SK (PCA+k-means) | ARI | 22.80±0.60 | #8 of 9 | Archive leaderboard | report |
| Unsupervised Image Classification | ImageNet | SimCLRv2 ResNet-152 + SK (PCA+k-means) | Accuracy (%) | 39.07±0.61 | #8 of 9 | Archive leaderboard | report |
| Unsupervised Image Classification | ObjectNet | InfoMin ResNeXt-152 + SK (PCA+k-means) | ARI | 1.59±0.04 | #1 of 2 | Archive leaderboard | report |
| Unsupervised Image Classification | ObjectNet | InfoMin ResNeXt-152 + SK (PCA+k-means) | Accuracy (%) | 6.53±0.19 | #1 of 2 | Archive leaderboard | report |
| Unsupervised Image Classification | ObjectNet | SimCLRv2 ResNet-152 + SK (PCA+k-means, 1500 clusters) | ARI | 1.32±0.05 | #2 of 2 | Archive leaderboard | report |
| Unsupervised Image Classification | ObjectNet | SimCLRv2 ResNet-152 + SK (PCA+k-means, 1500 clusters) | Accuracy (%) | 6.47±0.07 | #2 of 2 | 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.
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