Image Clustering On Cifar 100

Metriken

ARI

Accuracy

NMI

Train Set

Ergebnisse

Leistungsergebnisse verschiedener Modelle zu diesem Benchmark

Modellname	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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