Image Clustering On Cifar 100

评估指标

ARI

Accuracy

NMI

Train Set

评测结果

各个模型在此基准测试上的表现结果

模型名称	ARI	Accuracy	NMI	Train Set	Paper Title	Repository
TCL	0.357	0.531	0.529	Train	Twin Contrastive Learning for Online Clustering
HUME	0.377	0.555	-	Train	-	-
MMDC	-	0.446	0.418	-	Multi-Modal Deep Clustering: Unsupervised Partitioning of Images
RUC	-	-	-	Train	Improving Unsupervised Image Clustering With Robust Learning
IMC-SwAV (Avg+-)	0.337	0.49	0.503	-	Information Maximization Clustering via Multi-View Self-Labelling
ITAE	0.5053	0.6502	0.771	Test	Improving Image Clustering with Artifacts Attenuation via Inference-Time Attention Engineering	-
DeeperCluster	-	0.189	-	Train+Test	Deep Clustering for Unsupervised Learning of Visual Features
SPICE*	0.422	0.584	0.583	Train	SPICE: Semantic Pseudo-labeling for Image Clustering
DPAC	0.393	0.555	0.542	-	Deep Online Probability Aggregation Clustering	-
TEMI DINO ViT-B	0.533	0.671	0.769	Train	Exploring the Limits of Deep Image Clustering using Pretrained Models
JULE	-	0.137	0.103	Train+Test	Joint Unsupervised Learning of Deep Representations and Image Clusters
ConCURL	0.303	0.479	0.468	Train	Representation Learning for Clustering via Building Consensus
TEMI CLIP ViT-L (openai)	0.612	0.737	0.799	Train	Exploring the Limits of Deep Image Clustering using Pretrained Models
PRO-DSC	-	0.773	0.824	-	Exploring a Principled Framework For Deep Subspace Clustering
TURTLE (CLIP + DINOv2)	0.834	0.898	0.915	-	Let Go of Your Labels with Unsupervised Transfer
DEC	-	0.185	0.136	Train+Test	Unsupervised Deep Embedding for Clustering Analysis
CC	0.266	0.429	0.431	-	Contrastive Clustering
IDFD	0.264	0.425	0.426	Train	Clustering-friendly Representation Learning via Instance Discrimination and Feature Decorrelation
DCCM	-	0.327	0.285	Train+Test	Deep Comprehensive Correlation Mining for Image Clustering
CoHiClust	0.299	0.437	0.467	-	Contrastive Hierarchical Clustering

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