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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