Image Clustering
104 papers with code • 33 benchmarks • 21 datasets
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)
Benchmarks
These leaderboards are used to track progress in Image Clustering
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Libraries
Use these libraries to find Image Clustering models and implementationsDatasets
CIFAR-10
ImageNet
CIFAR-100
MNIST
Fashion-MNIST
CUB-200-2011
STL-10
Tiny ImageNet
Stanford Cars
USPS
Subtasks
Latest papers
Contrastive Tuning: A Little Help to Make Masked Autoencoders Forget
ml-jku/mae-ct
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In this work, we study how to combine the efficiency and scalability of MIM with the ability of ID to perform downstream classification in the absence of large amounts of labeled data.
Exploring the Limits of Deep Image Clustering using Pretrained Models
HHU-MMBS/TEMI-official-BMVC2023
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We present a general methodology that learns to classify images without labels by leveraging pretrained feature extractors.
Contrastive Hierarchical Clustering
michalznalezniak/contrastive-hierarchical-clustering
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Deep clustering has been dominated by flat models, which split a dataset into a predefined number of groups.
Local Connectivity-Based Density Estimation for Face Clustering
illian01/lce-pcenet
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CVPR 2023
For this purpose, we propose a reliable density estimation algorithm based on local connectivity between K nearest neighbors (KNN).
C3: Cross-instance guided Contrastive Clustering
Armanfard-Lab/C3
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In this paper, we propose a novel contrastive clustering method, Cross-instance guided Contrastive Clustering (C3), that considers the cross-sample relationships to increase the number of positive pairs and mitigate the impact of false negative, noise, and anomaly sample on the learned representation of data.
Twin Contrastive Learning for Online Clustering
Yunfan-Li/Twin-Contrastive-Learning
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Specifically, we find that when the data is projected into a feature space with a dimensionality of the target cluster number, the rows and columns of its feature matrix correspond to the instance and cluster representation, respectively.
Deep Fair Clustering via Maximizing and Minimizing Mutual Information: Theory, Algorithm and Metric
PengxinZeng/2023-CVPR-FCMI
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CVPR 2023
Fair clustering aims to divide data into distinct clusters while preventing sensitive attributes (\textit{e. g.}, gender, race, RNA sequencing technique) from dominating the clustering.
Improving Image Clustering through Sample Ranking and Its Application to remote--sensing images
qlilx/icsr
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Performing our method on a variety of datasets from remote sensing, we show that our method can be effectively applied to remote--sensing images.
Clustering Without Knowing How To: Application and Evaluation
Crowdsourcing allows running simple human intelligence tasks on a large crowd of workers, enabling solving problems for which it is difficult to formulate an algorithm or train a machine learning model in reasonable time.
Efficient Deep Clustering of Human Activities and How to Improve Evaluation
Lou1sM/HAR
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Asian Conference on Machine Learning 2023
Progress is starting to be made in the unsupervised setting, in the form of deep HAR clustering models, which can assign labels to data without having been given any labels to train on, but there are problems with evaluating deep HAR clustering models, which makes assessing the field and devising new methods difficult.
