Papers › Deep clustering with concrete k-means
Deep clustering with concrete k-means
Boyan Gao, Yongxin Yang, Henry Gouk, Timothy M. Hospedales
We address the problem of simultaneously learning a k-means clustering and deep feature representation from unlabelled data, which is of interest due to the potential of deep k-means to outperform traditional two-step feature extraction and shallow-clustering strategies. We achieve this by developing a gradient-estimator for the non-differentiable k-means objective via the Gumbel-Softmax reparameterisation trick. In contrast to previous attempts at deep clustering, our concrete k-means model can be optimised with respect to the canonical k-means objective and is easily trained end-to-end without resorting to alternating optimisation. We demonstrate the efficacy of our method on standard clustering benchmarks.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Online Clustering | cifar10 | CKM | online ACC | 15.2 | #2 of 3 | Archive leaderboard | report |
| Online Clustering | cifar10 | CKM | online ARI | 1.4 | #2 of 3 | Archive leaderboard | report |
| Online Clustering | cifar10 | CKM | online NMI | 2.8 | #2 of 3 | Archive leaderboard | report |
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