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Image Clustering On Imagenet 10

Metrics

Accuracy

NMI

Results

Performance results of various models on this benchmark

Model Name	Accuracy	NMI	Paper Title	Repository
JULE	0.300	0.175	Joint Unsupervised Learning of Deep Representations and Image Clusters	-
ConCURL	0.958	0.907	Representation Learning for Clustering via Building Consensus	-
DPAC	0.97	0.925	Deep Online Probability Aggregation Clustering	-
CoHiClust	0.953	0.907	Contrastive Hierarchical Clustering	-
VAE	0.334	0.193	Auto-Encoding Variational Bayes	-
SPICE (Full ImageNet pre-train)	0.969	0.927	SPICE: Semantic Pseudo-labeling for Image Clustering	-
TCL	0.895	0.875	Twin Contrastive Learning for Online Clustering	-
IDFD	0.954	0.898	Clustering-friendly Representation Learning via Instance Discrimination and Feature Decorrelation	-
MMDC	0.811	0.719	Multi-Modal Deep Clustering: Unsupervised Partitioning of Images	-
TAC	0.992	0.985	Image Clustering with External Guidance	-
ProPos*	0.962	0.908	Learning Representation for Clustering via Prototype Scattering and Positive Sampling	-
CC	0.893	0.859	Contrastive Clustering	-
DEC	0.381	0.282	Unsupervised Deep Embedding for Clustering Analysis	-
C3	0.942	0.905	C3: Cross-instance guided Contrastive Clustering	-
DCCM	0.71	0.608	Deep Comprehensive Correlation Mining for Image Clustering	-
DAC	0.527	0.394	Deep Adaptive Image Clustering
GAN	0.346	0.225	Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks	-
ProPos	0.956	0.896	Learning Representation for Clustering via Prototype Scattering and Positive Sampling	-

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Image Clustering On Imagenet 10 | SOTA | HyperAI