Image Clustering on Imagenet-dog-15

Metric: Accuracy (higher is better)

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#	Model↕	Accuracy▼	Extra Data	Paper	Date↕	Code
1	MAE-CT (best)	0.943	Yes	Contrastive Tuning: A Little Help to Make Masked...	2023-04-20	Code
2	MAE-CT (mean)	0.874	Yes	Contrastive Tuning: A Little Help to Make Masked...	2023-04-20	Code
3	PRO-DSC	0.84	Yes	Exploring a Principled Framework for Deep Subspa...	2025-03-21	Code
4	ProPos*	0.775	No	Learning Representation for Clustering via Proto...	2021-11-23	Code
5	ProPos	0.745	No	Learning Representation for Clustering via Proto...	2021-11-23	Code
6	DPAC	0.726	No	Deep Online Probability Aggregation Clustering	2024-07-07	Code
7	ConCURL	0.695	No	Representation Learning for Clustering via Build...	2021-05-04	Code
8	SPICE	0.675	No	SPICE: Semantic Pseudo-labeling for Image Cluste...	2021-03-17	Code
9	TCL	0.644	No	Twin Contrastive Learning for Online Clustering	2022-10-21	Code
10	IDFD	0.591	No	Clustering-friendly Representation Learning via ...	2021-05-31	Code
11	MiCE	0.439	No	MiCE: Mixture of Contrastive Experts for Unsuper...	2021-05-05	Code
12	C3	0.434	No	C3: Cross-instance guided Contrastive Clustering	2022-11-14	Code
13	CC	0.429	No	Contrastive Clustering	2020-09-21	Code
14	DCCM	0.383	No	Deep Comprehensive Correlation Mining for Image ...	2019-04-15	Code
15	CoHiClust	0.355	No	Contrastive Hierarchical Clustering	2023-03-03	Code
16	DAC	0.275	No	-	-	Code
17	DEC	0.195	No	Unsupervised Deep Embedding for Clustering Analy...	2015-11-19	Code
18	VAE	0.179	No	Auto-Encoding Variational Bayes	2013-12-20	Code
19	GAN	0.174	No	Unsupervised Representation Learning with Deep C...	2015-11-19	Code
20	JULE	0.138	No	Joint Unsupervised Learning of Deep Representati...	2016-04-13	Code

#1MAE-CT (best)SOTA
0.943
Accuracy· Extra Data· 2023-04-20
Contrastive Tuning: A Little Help to Make Masked Autoencoders Forget Code
#2MAE-CT (mean)
0.874
Accuracy· Extra Data· 2023-04-20
Contrastive Tuning: A Little Help to Make Masked Autoencoders Forget Code
#3PRO-DSC
0.84
Accuracy· Extra Data· 2025-03-21
Exploring a Principled Framework for Deep Subspace Clustering Code
#4ProPos*SOTA
0.775
Accuracy· 2021-11-23
Learning Representation for Clustering via Prototype Scattering and Positive Sampling Code
#5ProPos
0.745
Accuracy· 2021-11-23
Learning Representation for Clustering via Prototype Scattering and Positive Sampling Code
#6DPAC
0.726
Accuracy· 2024-07-07
Deep Online Probability Aggregation Clustering Code
#7ConCURLSOTA
0.695
Accuracy· 2021-05-04
Representation Learning for Clustering via Building Consensus Code
#8SPICESOTA
0.675
Accuracy· 2021-03-17
SPICE: Semantic Pseudo-labeling for Image Clustering Code
#9TCL
0.644
Accuracy· 2022-10-21
Twin Contrastive Learning for Online Clustering Code
#10IDFD
0.591
Accuracy· 2021-05-31
Clustering-friendly Representation Learning via Instance Discrimination and Feature Decorrelation Code
#11MiCE
0.439
Accuracy· 2021-05-05
MiCE: Mixture of Contrastive Experts for Unsupervised Image Clustering Code
#12C3
0.434
Accuracy· 2022-11-14
C3: Cross-instance guided Contrastive Clustering Code
#13CCSOTA
0.429
Accuracy· 2020-09-21
Contrastive Clustering Code
#14DCCMSOTA
0.383
Accuracy· 2019-04-15
Deep Comprehensive Correlation Mining for Image Clustering Code
#15CoHiClust
0.355
Accuracy· 2023-03-03
Contrastive Hierarchical Clustering Code
#16DAC
0.275
Accuracy
No paperCode
#17DECSOTA
0.195
Accuracy· 2015-11-19
Unsupervised Deep Embedding for Clustering Analysis Code
#18VAESOTA
0.179
Accuracy· 2013-12-20
Auto-Encoding Variational Bayes Code
#19GAN
0.174
Accuracy· 2015-11-19
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks Code
#20JULE
0.138
Accuracy· 2016-04-13
Joint Unsupervised Learning of Deep Representations and Image Clusters Code