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Image Clustering On Fashion Mnist

Metrics

Accuracy

NMI

Results

Performance results of various models on this benchmark

Model Name	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