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홈 뉴스 최신 연구 논문 튜토리얼 데이터셋 백과사전 SOTA LLM 모델 GPU 랭킹 컨퍼런스

한국어

HyperAI초신경

Image Clustering On Imagenet 10

평가 지표

Accuracy

NMI

평가 결과

이 벤치마크에서 각 모델의 성능 결과

모델 이름	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 | HyperAI초신경