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 with no code
Quantum Block-Matching Algorithm using Dissimilarity Measure
In this work, a measure that utilizes the quantum Fourier transform or the Swap test based on the Euclidean distance is proposed.
Bridging Distribution Learning and Image Clustering in High-dimensional Space
Based on the experimental results, we believe distribution learning can exploit the potential of GMM in image clustering within high-dimensional space.
MES-Loss: Mutually equidistant separation metric learning loss function
We propose in this paper a new composite DML loss function that, in addition to the intra-class compactness, explicitly implies regulations to enforce the best inter-class separation by mutually equidistantly distributing the centers of the classes.
A Provable Splitting Approach for Symmetric Nonnegative Matrix Factorization
The symmetric Nonnegative Matrix Factorization (NMF), a special but important class of the general NMF, has found numerous applications in data analysis such as various clustering tasks.
Contrastive learning for unsupervised medical image clustering and reconstruction
The lack of large labeled medical imaging datasets, along with significant inter-individual variability compared to clinically established disease classes, poses significant challenges in exploiting medical imaging information in a precision medicine paradigm, where in principle dense patient-specific data can be employed to formulate individual predictions and/or stratify patients into finer-grained groups which may follow more homogeneous trajectories and therefore empower clinical trials.
Self-supervised Image Clustering from Multiple Incomplete Views via Constrastive Complementary Generation
Incomplete Multi-View Clustering aims to enhance clustering performance by using data from multiple modalities.
Joint Debiased Representation and Image Clustering Learning with Self-Supervision
However, existing methods for joint clustering and contrastive learning do not perform well on long-tailed data distributions, as majority classes overwhelm and distort the loss of minority classes, thus preventing meaningful representations to be learned.
Semantic-Enhanced Image Clustering
In this paper, we propose to investigate the task of image clustering with the help of a visual-language pre-training model.
Deep embedded clustering algorithm for clustering PACS repositories
This, however, requires an efficient method for learning latent image representations.
Attention-based Dynamic Subspace Learners for Medical Image Analysis
This integrated attention mechanism provides a visual insight of discriminative image features that contribute to the clustering of image sets and a visual explanation of the embedding features.
