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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

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
1MAE-CT (best)Accuracy0.94—Unverified
2MAE-CT (mean)Accuracy0.87—Unverified
3PRO-DSCAccuracy0.84—Unverified
4ProPos*Accuracy0.78—Unverified
5ProPosAccuracy0.75—Unverified
6DPACAccuracy0.73—Unverified
7ConCURLAccuracy0.7—Unverified
8SPICEAccuracy0.68—Unverified
9TCLAccuracy0.64—Unverified
10IDFDAccuracy0.59—Unverified