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Image Clustering On Fashion Mnist

Métriques

Accuracy

NMI

Résultats

Résultats de performance de divers modèles sur ce benchmark

Nom du modèle	Accuracy	NMI	Paper Title	Repository
DDC	0.619	0.682	Deep Density-based Image Clustering	-
DTI-Clustering	0.612	0.637	Deep Transformation-Invariant Clustering	-
SNNL-4	0.555	0.574	Improving k-Means Clustering Performance with Disentangled Internal Representations	-
DEN	0.635	0.71	Interpretable Visualizations with Differentiating Embedding Networks	-
PSSC	0.628	0.644	Scattering Transform Based Image Clustering using Projection onto Orthogonal Complement	-
CoHiClust	0.65	-	Contrastive Hierarchical Clustering	-
SPC	0.679	0.735	Selective Pseudo-label Clustering	-
N2D (UMAP)	0.672	0.684	N2D: (Not Too) Deep Clustering via Clustering the Local Manifold of an Autoencoded Embedding	-
DDC-DA	0.609	0.661	Deep Density-based Image Clustering	-
GDL	0.627	0.66	Graph Degree Linkage: Agglomerative Clustering on a Directed Graph	-
DynAE	0.591	0.642	Deep Clustering with a Dynamic Autoencoder: From Reconstruction towards Centroids Construction	-
PRCut (DinoV2)	0.791	0.758	Deep Clustering via Probabilistic Ratio-Cut Optimization
VMM	0.716	0.710	The VampPrior Mixture Model	-

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Image Clustering On Fashion Mnist | SOTA | HyperAI