Image Clustering On Cifar 100

평가 지표

ARI

Accuracy

NMI

Train Set

평가 결과

이 벤치마크에서 각 모델의 성능 결과

모델 이름	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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