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SOTA
图像聚类
Image Clustering On Mnist Test
Image Clustering On Mnist Test
评估指标
Accuracy
NMI
评测结果
各个模型在此基准测试上的表现结果
Columns
模型名称
Accuracy
NMI
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
0 of 11 row(s) selected.
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HyperAI
HyperAI超神经
首页
算力平台
文档
资讯
论文
教程
数据集
百科
SOTA
LLM 模型天梯
GPU 天梯
顶会
开源项目
全站搜索
关于
服务条款
隐私政策
中文
HyperAI
HyperAI超神经
Toggle Sidebar
全站搜索…
⌘
K
Command Palette
Search for a command to run...
算力平台
首页
SOTA
图像聚类
Image Clustering On Mnist Test
Image Clustering On Mnist Test
评估指标
Accuracy
NMI
评测结果
各个模型在此基准测试上的表现结果
Columns
模型名称
Accuracy
NMI
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
0 of 11 row(s) selected.
Previous
Next
Image Clustering On Mnist Test | SOTA | HyperAI超神经