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Image Clustering On Fashion Mnist

評価指標

Accuracy

NMI

評価結果

このベンチマークにおける各モデルのパフォーマンス結果

			Paper Title
PRCut (DinoV2)	0.791	0.758	Deep Clustering via Probabilistic Ratio-Cut Optimization
VMM	0.716	0.710	The VampPrior Mixture Model
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
CoHiClust	0.65	-	Contrastive Hierarchical Clustering
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
GDL	0.627	0.66	Graph Degree Linkage: Agglomerative Clustering on a Directed Graph
DDC	0.619	0.682	Deep Density-based Image Clustering
DTI-Clustering	0.612	0.637	Deep Transformation-Invariant Clustering
DDC-DA	0.609	0.661	Deep Density-based Image Clustering
DynAE	0.591	0.642	Deep Clustering with a Dynamic Autoencoder: From Reconstruction towards Centroids Construction
SNNL-4	0.555	0.574	Improving k-Means Clustering Performance with Disentangled Internal Representations

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Image Clustering On Fashion Mnist | SOTA | HyperAI超神経