Image Clustering On Mnist Test

Metriken

Accuracy

NMI

Ergebnisse

Leistungsergebnisse verschiedener Modelle zu diesem Benchmark

			Paper Title
DynAE	0.987	0.963	Deep Clustering with a Dynamic Autoencoder: From Reconstruction towards Centroids Construction
DTI-Clustering	0.978	0.947	Deep Transformation-Invariant Clustering
DDC-DA	0.97	0.927	Deep Density-based Image Clustering
PSSC	0.967	0.919	Scattering Transform Based Image Clustering using Projection onto Orthogonal Complement
DDC	0.965	0.916	Deep Density-based Image Clustering
OURS-RC	-	0.915	Joint Unsupervised Learning of Deep Representations and Image Clusters
GDL	-	0.91	Graph Degree Linkage: Agglomerative Clustering on a Directed Graph
AE+SNNL	0.962	0.903	Improving k-Means Clustering Performance with Disentangled Internal Representations
N2D (UMAP)	0.948	0.882	N2D: (Not Too) Deep Clustering via Clustering the Local Manifold of an Autoencoded Embedding
SR-K-means	0.863	0.873	Deep clustering: On the link between discriminative models and K-means
AGDL	-	0.844	Graph Degree Linkage: Agglomerative Clustering on a Directed Graph

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Image Clustering On Mnist Test | SOTA | HyperAI