Image Clustering On Usps

Metriken

Accuracy

NMI

Ergebnisse

Leistungsergebnisse verschiedener Modelle zu diesem Benchmark

			Paper Title
SPC	0.984	0.954	Selective Pseudo-label Clustering
DynAE	0.981	0.948	Deep Clustering with a Dynamic Autoencoder: From Reconstruction towards Centroids Construction
DEN	0.979	0.944	Interpretable Visualizations with Differentiating Embedding Networks
DDC-DA	0.977	0.939	Deep Density-based Image Clustering
SR-K-means	0.974	0.936	Deep clustering: On the link between discriminative models and K-means
ClusterGAN	0.970	0.931	Balanced Self-Paced Learning for Generative Adversarial Clustering Network
DMSC	0.951	0.929	Deep Multimodal Subspace Clustering Networks
DDC	0.967	0.918	Deep Density-based Image Clustering
JULE-RC	-	0.913	Joint Unsupervised Learning of Deep Representations and Image Clusters
N2D (UMAP)	0.958	0.901	N2D: (Not Too) Deep Clustering via Clustering the Local Manifold of an Autoencoded Embedding
PSSC	0.957	0.898	Scattering Transform Based Image Clustering using Projection onto Orthogonal Complement
Tree-SNE	-	0.885	Tree-SNE: Hierarchical Clustering and Visualization Using t-SNE
DTI-Clustering	0.864	0.882	Deep Transformation-Invariant Clustering
DnC-SC	0.8255	0.8286	-
AGDL	-	0.824	Graph Degree Linkage: Agglomerative Clustering on a Directed Graph
DBC	0.743	0.724	Discriminatively Boosted Image Clustering with Fully Convolutional Auto-Encoders

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Image Clustering On Usps | SOTA | HyperAI