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SOTA
Bildclustering
Image Clustering On Imagenet 10
Image Clustering On Imagenet 10
Metriken
Accuracy
NMI
Ergebnisse
Leistungsergebnisse verschiedener Modelle zu diesem Benchmark
Columns
Modellname
Accuracy
NMI
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 | HyperAI