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Image Clustering On Tiny Imagenet

Métriques

Accuracy

NMI

Résultats

Résultats de performance de divers modèles sur ce benchmark

Nom du modèle	Accuracy	NMI	Paper Title	Repository
DAC	0.066	0.190	Deep Adaptive Image Clustering
MMDC	0.119	0.274	Multi-Modal Deep Clustering: Unsupervised Partitioning of Images	-
GAN	0.041	0.135	Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks	-
PRO-DSC	0.698	0.805	Exploring a Principled Framework For Deep Subspace Clustering
VAE	0.036	0.113	Auto-Encoding Variational Bayes	-
SPICE	0.305	0.449	SPICE: Semantic Pseudo-labeling for Image Clustering	-
ITAE	0.6823	0.8178	Improving Image Clustering with Artifacts Attenuation via Inference-Time Attention Engineering	-
DEC	0.037	0.115	Unsupervised Deep Embedding for Clustering Analysis	-
CC	0.14	0.34	Contrastive Clustering	-
C3	0.141	0.335	C3: Cross-instance guided Contrastive Clustering	-
IMC-SwAV (Best)	0.282	0.526	Information Maximization Clustering via Multi-View Self-Labelling	-
DCCM	0.108	0.224	Deep Comprehensive Correlation Mining for Image Clustering	-
JULE	0.033	0.102	Joint Unsupervised Learning of Deep Representations and Image Clusters	-
IMC-SwAV (Avg+-)	0.279	0.485	Information Maximization Clustering via Multi-View Self-Labelling	-

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Image Clustering On Tiny Imagenet | SOTA | HyperAI