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

Paper TitleRepository
TURTLE (CLIP + DINOv2)0.997-Let Go of Your Labels with Unsupervised Transfer
TEMI DINO ViT-B0.985ViT-BExploring the Limits of Deep Image Clustering using Pretrained Models
TAC0.982-Image Clustering with External Guidance
SPICE-BPA0.943ResNet-34The Balanced-Pairwise-Affinities Feature Transform
DPAC0.934ResNet-34Deep Online Probability Aggregation Clustering
SPICE*0.929ResNet-34SPICE: Semantic Pseudo-labeling for Image Clustering
HUME0.908ResNet-18--
TCL0.868ResNet-34Twin Contrastive Learning for Online Clustering
RUC0.867ResNet-18Improving Unsupervised Image Clustering With Robust Learning
IMC-SwAV (Best)0.853ResNet-18Information Maximization Clustering via Multi-View Self-Labelling
CC0.85ResNet34Contrastive Clustering
SeCu0.836ResNet-18Stable Cluster Discrimination for Deep Clustering
IMC-SwAV (Avg+-)0.831ResNet-18Information Maximization Clustering via Multi-View Self-Labelling
ITAE0.8276ViT-B/14Improving Image Clustering with Artifacts Attenuation via Inference-Time Attention Engineering-
SCAN0.809ResNet-18SCAN: Learning to Classify Images without Labels
SCAN (Avg)0.767ResNet-18SCAN: Learning to Classify Images without Labels
IDFD0.756ResNet-18Clustering-friendly Representation Learning via Instance Discrimination and Feature Decorrelation
MiCE0.752ResNet-34MiCE: Mixture of Contrastive Experts for Unsupervised Image Clustering
ConCURL0.749-Representation Learning for Clustering via Building Consensus
MMDC0.694ResNet18Multi-Modal Deep Clustering: Unsupervised Partitioning of Images
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Image Clustering On Stl 10 | SOTA | HyperAI超神经