Image Clustering On Imagenet Dog 15
评估指标
ARI
Accuracy
Backbone
NMI
评测结果
各个模型在此基准测试上的表现结果
比较表格
| 模型名称 | ARI | Accuracy | Backbone | NMI |
|---|---|---|---|---|
| deep-probability-aggregation-clustering | 0.598 | 0.726 | ResNet-34 | 0.667 |
| c3-cross-instance-guided-contrastive | 0.28 | 0.434 | - | 0.448 |
| deep-adaptive-image-clustering | - | 0.275 | - | 0.219 |
| deep-comprehensive-correlation-mining-for | - | 0.383 | - | 0.321 |
| mice-mixture-of-contrastive-experts-for-1 | 0.286 | 0.439 | - | 0.423 |
| unsupervised-deep-embedding-for-clustering | - | 0.195 | - | 0.122 |
| contrastive-tuning-a-little-help-to-make | 0.879 | 0.943 | ViT-H/16 | 0.904 |
| contrastive-clustering | 0.274 | 0.429 | - | 0.445 |
| representation-learning-for-clustering-via | 0.531 | 0.695 | - | 0.63 |
| twin-contrastive-learning-for-online | 0.516 | 0.644 | - | 0.623 |
| exploring-non-contrastive-representation-1 | 0.675 | 0.775 | ResNet-34 | 0.737 |
| clustering-friendly-representation-learning-1 | 0.413 | 0.591 | - | 0.546 |
| auto-encoding-variational-bayes | - | 0.179 | - | 0.107 |
| exploring-a-principled-framework-for-deep | - | 0.840 | - | 0.812 |
| unsupervised-representation-learning-with-1 | - | 0.174 | - | 0.121 |
| spice-semantic-pseudo-labeling-for-image | 0.526 | 0.675 | - | 0.627 |
| contrastive-hierarchical-clustering | 0.232 | 0.355 | ResNet-50 | 0.411 |
| joint-unsupervised-learning-of-deep | - | 0.138 | - | 0.054 |
| contrastive-tuning-a-little-help-to-make | 0.821 | 0.874 | ViT-H/16 | 0.882 |
| exploring-non-contrastive-representation-1 | 0.627 | 0.745 | ResNet-34 | 0.692 |