SOTAVerified

Image Clustering

Models that partition the dataset into semantically meaningful clusters without having access to the ground truth labels.

Image credit: ImageNet clustering results of SCAN: Learning to Classify Images without Labels (ECCV 2020)

Papers

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Showing 1–10 of 236 papers

TitleStatusHype
Structural-Spectral Graph Convolution with Evidential Edge Learning for Hyperspectral Image ClusteringCode0
Unsupervised Deep Clustering of MNIST with Triplet-Enhanced Convolutional Autoencoders—0
Advanced Clustering Framework for Semiconductor Image Analytics Integrating Deep TDA with Self-Supervised and Transfer Learning Techniques—0
Utilization of Neighbor Information for Image Classification with Different Levels of Supervision—0
Online Meta-learning for AutoML in Real-time (OnMAR)—0
Keep It Light! Simplifying Image Clustering Via Text-Free Adapters—0
Deep Clustering via Probabilistic Ratio-Cut OptimizationCode0
DiFiC: Your Diffusion Model Holds the Secret to Fine-Grained Clustering—0
Graph Cut-guided Maximal Coding Rate Reduction for Learning Image Embedding and ClusteringCode0
I Spy With My Little Eye: A Minimum Cost Multicut Investigation of Dataset FramesCode0
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All datasetsCIFAR-10CIFAR-100STL-10Imagenet-dog-15ImageNet-10MNIST-fullUSPSTiny ImageNetFashion-MNISTImageNetMNIST-testcoil-100

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1A-DSSC (Scattered)NMI1—Unverified
2J-DSSC (Scattered)NMI0.99—Unverified
3JULE-RCNMI0.99—Unverified
4A-DSSCNMI0.95—Unverified
5J-DSSCNMI0.94—Unverified
6GDL-UNMI0.93—Unverified
7Tree-SNENMI0.93—Unverified
8DBCNMI0.91—Unverified
9GDLAccuracy0.73—Unverified
10EnSCAccuracy0.69—Unverified