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HyperAI超神经

Image Clustering On Tiny Imagenet

评估指标

Accuracy

NMI

评测结果

各个模型在此基准测试上的表现结果

模型名称	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超神经