Image Clustering on MNIST-test

Metric: Accuracy (higher is better)

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#	Model↕	Accuracy▼	Extra Data	Paper	Date↕	Code
1	DynAE	0.987	No	Deep Clustering with a Dynamic Autoencoder: From...	2019-01-23	Code
2	DTI-Clustering	0.978	No	Deep Transformation-Invariant Clustering	2020-06-19	Code
3	DDC-DA	0.97	No	Deep Density-based Image Clustering	2018-12-11	Code
4	PSSC	0.967	No	Scattering Transform Based Image Clustering usin...	2020-11-23	Code
5	DDC	0.965	No	Deep Density-based Image Clustering	2018-12-11	Code
6	AE+SNNL	0.962	No	Improving k-Means Clustering Performance with Di...	2020-06-05	Code
7	N2D (UMAP)	0.948	No	N2D: (Not Too) Deep Clustering via Clustering th...	2019-08-16	Code
8	SR-K-means	0.863	No	Deep clustering: On the link between discriminat...	2018-10-09	Code

#1DynAESOTA
0.987
Accuracy· 2019-01-23
Deep Clustering with a Dynamic Autoencoder: From Reconstruction towards Centroids Construction Code
#2DTI-Clustering
0.978
Accuracy· 2020-06-19
Deep Transformation-Invariant Clustering Code
#3DDC-DASOTA
0.97
Accuracy· 2018-12-11
Deep Density-based Image Clustering Code
#4PSSC
0.967
Accuracy· 2020-11-23
Scattering Transform Based Image Clustering using Projection onto Orthogonal Complement Code
#5DDC
0.965
Accuracy· 2018-12-11
Deep Density-based Image Clustering Code
#6AE+SNNL
0.962
Accuracy· 2020-06-05
Improving k-Means Clustering Performance with Disentangled Internal Representations Code
#7N2D (UMAP)
0.948
Accuracy· 2019-08-16
N2D: (Not Too) Deep Clustering via Clustering the Local Manifold of an Autoencoded Embedding Code
#8SR-K-meansSOTA
0.863
Accuracy· 2018-10-09
Deep clustering: On the link between discriminative models and K-means Code