Papers › Improving Unsupervised Image Clustering With Robust Learning
Improving Unsupervised Image Clustering With Robust Learning
Sungwon Park, Sungwon Han, Sundong Kim, Danu Kim, Sungkyu Park, Seunghoon Hong, Meeyoung Cha
Unsupervised image clustering methods often introduce alternative objectives to indirectly train the model and are subject to faulty predictions and overconfident results. To overcome these challenges, the current research proposes an innovative model RUC that is inspired by robust learning. RUC's novelty is at utilizing pseudo-labels of existing image clustering models as a noisy dataset that may include misclassified samples. Its retraining process can revise misaligned knowledge and alleviate the overconfidence problem in predictions. The model's flexible structure makes it possible to be used as an add-on module to other clustering methods and helps them achieve better performance on multiple datasets. Extensive experiments show that the proposed model can adjust the model confidence with better calibration and gain additional robustness against adversarial noise.
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Code
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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 | RUC | Accuracy | 0.903 | #13 of 40 | Archive leaderboard | report |
| Image Clustering | CIFAR-10 | RUC | Backbone | ResNet-18 | #13 of 40 | Archive leaderboard | report |
| Image Clustering | CIFAR-100 | RUC | Train Set | Train | #30 of 30 | Archive leaderboard | report |
| Image Clustering | STL-10 | RUC | Accuracy | 0.867 | #9 of 29 | Archive leaderboard | report |
| Image Clustering | STL-10 | RUC | Backbone | ResNet-18 | #9 of 29 | Archive leaderboard | report |
| Unsupervised Image Classification | CIFAR-10 | RUC | Accuracy | 90.3 | #3 of 9 | Archive leaderboard | report |
| Unsupervised Image Classification | CIFAR-20 | RUC | Accuracy | 54.3 | #9 of 14 | Archive leaderboard | report |
| Unsupervised Image Classification | STL-10 | RUC | Accuracy | 86.7 | #4 of 9 | 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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