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

模型名称
Accuracy
Backbone
Paper TitleRepository
RUC0.867ResNet-18Improving Unsupervised Image Clustering With Robust Learning-
CC0.85ResNet34Contrastive Clustering-
VAE0.282-Auto-Encoding Variational Bayes-
IMC-SwAV (Best)0.853ResNet-18Information Maximization Clustering via Multi-View Self-Labelling-
DCCM0.482AlexNetDeep Comprehensive Correlation Mining for Image Clustering-
SPICE*0.929ResNet-34SPICE: Semantic Pseudo-labeling for Image Clustering-
MiCE0.752ResNet-34MiCE: Mixture of Contrastive Experts for Unsupervised Image Clustering-
TAC0.982-Image Clustering with External Guidance-
TURTLE (CLIP + DINOv2)0.997-Let Go of Your Labels with Unsupervised Transfer-
CoHiClust0.613ResNet-50Contrastive Hierarchical Clustering-
SCAN (Avg)0.767ResNet-18SCAN: Learning to Classify Images without Labels-
SeCu0.836ResNet-18Stable Cluster Discrimination for Deep Clustering-
ConCURL0.749-Representation Learning for Clustering via Building Consensus-
MMDC0.694ResNet18Multi-Modal Deep Clustering: Unsupervised Partitioning of Images-
ITAE0.8276ViT-B/14Improving Image Clustering with Artifacts Attenuation via Inference-Time Attention Engineering-
DAC0.470ConvNetDeep Adaptive Image Clustering
IDFD0.756ResNet-18Clustering-friendly Representation Learning via Instance Discrimination and Feature Decorrelation-
SPICE-BPA0.943ResNet-34The Balanced-Pairwise-Affinities Feature Transform-
TCL0.868ResNet-34Twin Contrastive Learning for Online Clustering-
MCR20.491-Learning Diverse and Discriminative Representations via the Principle of Maximal Coding Rate Reduction-
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Image Clustering On Stl 10 | SOTA | HyperAI超神经