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
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
In recent years, supervised learning with convolutional networks (CNNs) has seen huge adoption in computer vision applications.
Auto-Encoding Variational Bayes
First, we show that a reparameterization of the variational lower bound yields a lower bound estimator that can be straightforwardly optimized using standard stochastic gradient methods.
Unsupervised Deep Embedding for Clustering Analysis
piiswrong/dec
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Clustering is central to many data-driven application domains and has been studied extensively in terms of distance functions and grouping algorithms.
Deep Clustering for Unsupervised Learning of Visual Features
facebookresearch/deepcluster
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ECCV 2018
In this work, we present DeepCluster, a clustering method that jointly learns the parameters of a neural network and the cluster assignments of the resulting features.
Invariant Information Clustering for Unsupervised Image Classification and Segmentation
The method is not specialised to computer vision and operates on any paired dataset samples; in our experiments we use random transforms to obtain a pair from each image.
N2D: (Not Too) Deep Clustering via Clustering the Local Manifold of an Autoencoded Embedding
rymc/n2d
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We study a number of local and global manifold learning methods on both the raw data and autoencoded embedding, concluding that UMAP in our framework is best able to find the most clusterable manifold in the embedding, suggesting local manifold learning on an autoencoded embedding is effective for discovering higher quality discovering clusters.
Sparse Subspace Clustering: Algorithm, Theory, and Applications
panji1990/Deep-subspace-clustering-networks
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In this paper, we propose and study an algorithm, called Sparse Subspace Clustering (SSC), to cluster data points that lie in a union of low-dimensional subspaces.
Self-labelling via simultaneous clustering and representation learning
yukimasano/self-label
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ICLR 2020
Combining clustering and representation learning is one of the most promising approaches for unsupervised learning of deep neural networks.
Joint Unsupervised Learning of Deep Representations and Image Clusters
jwyang/joint-unsupervised-learning
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CVPR 2016
In this paper, we propose a recurrent framework for Joint Unsupervised LEarning (JULE) of deep representations and image clusters.
Deep Subspace Clustering Networks
panji1990/Deep-subspace-clustering-networks
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NeurIPS 2017
We present a novel deep neural network architecture for unsupervised subspace clustering.
