Image Clustering On Imagenet Dog 15

Métriques

ARI

Accuracy

Backbone

NMI

Résultats

Résultats de performance de divers modèles sur ce benchmark

Nom du modèle	ARI	Accuracy	Backbone	NMI	Paper Title	Repository
DPAC	0.598	0.726	ResNet-34	0.667	Deep Online Probability Aggregation Clustering	-
C3	0.28	0.434	-	0.448	C3: Cross-instance guided Contrastive Clustering	-
DAC	-	0.275	-	0.219	Deep Adaptive Image Clustering
DCCM	-	0.383	-	0.321	Deep Comprehensive Correlation Mining for Image Clustering	-
MiCE	0.286	0.439	-	0.423	MiCE: Mixture of Contrastive Experts for Unsupervised Image Clustering	-
DEC	-	0.195	-	0.122	Unsupervised Deep Embedding for Clustering Analysis	-
MAE-CT (best)	0.879	0.943	ViT-H/16	0.904	Contrastive Tuning: A Little Help to Make Masked Autoencoders Forget	-
CC	0.274	0.429	-	0.445	Contrastive Clustering	-
ConCURL	0.531	0.695	-	0.63	Representation Learning for Clustering via Building Consensus	-
TCL	0.516	0.644	-	0.623	Twin Contrastive Learning for Online Clustering	-
ProPos*	0.675	0.775	ResNet-34	0.737	Learning Representation for Clustering via Prototype Scattering and Positive Sampling	-
IDFD	0.413	0.591	-	0.546	Clustering-friendly Representation Learning via Instance Discrimination and Feature Decorrelation	-
VAE	-	0.179	-	0.107	Auto-Encoding Variational Bayes	-
PRO-DSC	-	0.840	-	0.812	Exploring a Principled Framework For Deep Subspace Clustering
GAN	-	0.174	-	0.121	Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks	-
SPICE	0.526	0.675	-	0.627	SPICE: Semantic Pseudo-labeling for Image Clustering	-
CoHiClust	0.232	0.355	ResNet-50	0.411	Contrastive Hierarchical Clustering	-
JULE	-	0.138	-	0.054	Joint Unsupervised Learning of Deep Representations and Image Clusters	-
MAE-CT (mean)	0.821	0.874	ViT-H/16	0.882	Contrastive Tuning: A Little Help to Make Masked Autoencoders Forget	-
ProPos	0.627	0.745	ResNet-34	0.692	Learning Representation for Clustering via Prototype Scattering and Positive Sampling	-

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