Papers › Unsupervised Visual Representation Learning by Online Constrained K-Means
Unsupervised Visual Representation Learning by Online Constrained K-Means
Qi Qian, Yuanhong Xu, Juhua Hu, Hao Li, Rong Jin
Cluster discrimination is an effective pretext task for unsupervised representation learning, which often consists of two phases: clustering and discrimination. Clustering is to assign each instance a pseudo label that will be used to learn representations in discrimination. The main challenge resides in clustering since prevalent clustering methods (e.g., k-means) have to run in a batch mode. Besides, there can be a trivial solution consisting of a dominating cluster. To address these challenges, we first investigate the objective of clustering-based representation learning. Based on this, we propose a novel clustering-based pretext task with online \textbf{Co}nstrained \textbf{K}-m\textbf{e}ans (\textbf{CoKe}). Compared with the balanced clustering that each cluster has exactly the same size, we only constrain the minimal size of each cluster to flexibly capture the inherent data structure. More importantly, our online assignment method has a theoretical guarantee to approach the global optimum. By decoupling clustering and discrimination, CoKe can achieve competitive performance when optimizing with only a single view from each instance. Extensive experiments on ImageNet and other benchmark data sets verify both the efficacy and efficiency of our proposal. Code is available at \url{https://github.com/idstcv/CoKe}.
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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 |
|---|---|---|---|---|---|---|---|
| Image Clustering | CIFAR-10 | CoKe | ARI | 0.732 | #20 of 40 | Archive leaderboard | report |
| Image Clustering | CIFAR-10 | CoKe | Accuracy | 0.857 | #20 of 40 | Archive leaderboard | report |
| Image Clustering | CIFAR-10 | CoKe | Backbone | ResNet-18 | #20 of 40 | Archive leaderboard | report |
| Image Clustering | CIFAR-10 | CoKe | NMI | 0.766 | #20 of 40 | Archive leaderboard | report |
| Image Clustering | CIFAR-10 | CoKe | Train set | Train | #20 of 40 | Archive leaderboard | report |
| Self-Supervised Image Classification | ImageNet | CoKe (ResNet-50) | Number of Params | 25M | #61 of 144 | Archive leaderboard | report |
| Self-Supervised Image Classification | ImageNet | CoKe (ResNet-50) | Top 1 Accuracy | 76.4% | #61 of 144 | Archive leaderboard | report |
| Unsupervised Image Classification | CIFAR-10 | CoKe | Accuracy | 85.7 | #6 of 9 | Archive leaderboard | report |
| Unsupervised Image Classification | CIFAR-20 | CoKe | Accuracy | 49.7 | #12 of 14 | 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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