Image Clustering On Stl 10
评估指标
Accuracy
Backbone
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 | Accuracy | Backbone |
---|---|---|
improving-unsupervised-image-clustering-with | 0.867 | ResNet-18 |
contrastive-clustering | 0.85 | ResNet34 |
auto-encoding-variational-bayes | 0.282 | - |
information-maximization-clustering-via-multi | 0.853 | ResNet-18 |
deep-comprehensive-correlation-mining-for | 0.482 | AlexNet |
spice-semantic-pseudo-labeling-for-image | 0.929 | ResNet-34 |
mice-mixture-of-contrastive-experts-for-1 | 0.752 | ResNet-34 |
image-clustering-with-external-guidance | 0.982 | - |
let-go-of-your-labels-with-unsupervised-1 | 0.997 | - |
contrastive-hierarchical-clustering | 0.613 | ResNet-50 |
learning-to-classify-images-without-labels | 0.767 | ResNet-18 |
stable-cluster-discrimination-for-deep-1 | 0.836 | ResNet-18 |
representation-learning-for-clustering-via | 0.749 | - |
multi-modal-deep-clustering-unsupervised | 0.694 | ResNet18 |
improving-image-clustering-with-artifacts | 0.8276 | ViT-B/14 |
deep-adaptive-image-clustering | 0.470 | ConvNet |
clustering-friendly-representation-learning-1 | 0.756 | ResNet-18 |
the-balanced-pairwise-affinities-feature | 0.943 | ResNet-34 |
twin-contrastive-learning-for-online | 0.868 | ResNet-34 |
learning-diverse-and-discriminative | 0.491 | - |
learning-to-classify-images-without-labels | 0.809 | ResNet-18 |
模型 22 | 0.908 | ResNet-18 |
unsupervised-representation-learning-with-1 | 0.298 | - |
joint-unsupervised-learning-of-deep | 0.277 | - |
information-maximization-clustering-via-multi | 0.831 | ResNet-18 |
unsupervised-deep-embedding-for-clustering | 0.359 | - |
exploring-the-limits-of-deep-image-clustering | 0.985 | ViT-B |
mitigating-embedding-and-class-assignment | 0.665 | ResNet-18 |
deep-probability-aggregation-clustering | 0.934 | ResNet-34 |