Image Clustering On Imagenet Dog 15

Metrics

ARI

Accuracy

Backbone

NMI

Results

Performance results of various models on this benchmark

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

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Image Clustering On Imagenet Dog 15

Metrics

Results