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
Most implemented papers
Deep Multimodal Subspace Clustering Networks
mahdiabavisani/Deep-multimodal-subspace-clustering-networks
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In addition to various spatial fusion-based methods, an affinity fusion-based network is also proposed in which the self-expressive layer corresponding to different modalities is enforced to be the same.
Image Clustering with Optimization Algorithms and Color Space
In this study, a new color image clustering algorithm with multilevel thresholding has been presented and, it has been shown how to use the multilevel thresholding techniques for color image clustering.
Improving Image Clustering With Multiple Pretrained CNN Feature Extractors
For many image clustering problems, replacing raw image data with features extracted by a pretrained convolutional neural network (CNN), leads to better clustering performance.
Decipherment of Historical Manuscript Images
European libraries and archives are filled with enciphered manuscripts from the early modern period.
Deep clustering: On the link between discriminative models and K-means
Typically, they use multinomial logistic regression posteriors and parameter regularization, as is very common in supervised learning.
Accurate and Scalable Image Clustering Based On Sparse Representation of Camera Fingerprint
In this paper, we propose an accurate clustering framework, which exploits linear dependencies among SPNs in their intrinsic vector subspaces.
FineGAN: Unsupervised Hierarchical Disentanglement for Fine-Grained Object Generation and Discovery
kkanshul/finegan
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CVPR 2019
We propose FineGAN, a novel unsupervised GAN framework, which disentangles the background, object shape, and object appearance to hierarchically generate images of fine-grained object categories.
Deep Density-based Image Clustering
Recently, deep clustering, which is able to perform feature learning that favors clustering tasks via deep neural networks, has achieved remarkable performance in image clustering applications.
Dynamic Graph-Based Label Propagation for Density Peaks Clustering
The cut-off distance affects the local density values and is calculated in different ways depending on the size of the datasets, which can influence the quality of clustering.
Deep Clustering with a Dynamic Autoencoder: From Reconstruction towards Centroids Construction
nairouz/DynAE
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In unsupervised learning, there is no apparent straightforward cost function that can capture the significant factors of variations and similarities.
