HyperAI초신경

Image Clustering On Imagenet 10

평가 지표

Accuracy

NMI

평가 결과

이 벤치마크에서 각 모델의 성능 결과

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

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Image Clustering On Imagenet 10 | SOTA | HyperAI초신경