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SOTA
Image Clustering
Image Clustering On Tiny Imagenet
Image Clustering On Tiny Imagenet
Métriques
Accuracy
NMI
Résultats
Résultats de performance de divers modèles sur ce benchmark
Columns
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
0 of 14 row(s) selected.
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