Image Clustering

74 papers with code • 30 benchmarks • 18 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)

Libraries

Use these libraries to find Image Clustering models and implementations

Most implemented papers

Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks

tensorflow/models 19 Nov 2015

In recent years, supervised learning with convolutional networks (CNNs) has seen huge adoption in computer vision applications.

Auto-Encoding Variational Bayes

microsoft/recommenders 20 Dec 2013

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

elieJalbout/Clustering-with-Deep-learning 19 Nov 2015

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 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

xu-ji/IIC ICCV 2019

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.

Sparse Subspace Clustering: Algorithm, Theory, and Applications

panji1990/Deep-subspace-clustering-networks 5 Mar 2012

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.

N2D: (Not Too) Deep Clustering via Clustering the Local Manifold of an Autoencoded Embedding

rymc/n2d 16 Aug 2019

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.

Self-labelling via simultaneous clustering and representation learning

yukimasano/self-label ICLR 2020

Combining clustering and representation learning is one of the most promising approaches for unsupervised learning of deep neural networks.

An Image Clustering Auto-Encoder Based on Predefined Evenly-Distributed Class Centroids and MMD Distance

zyWang-Power/Clustering 10 Jun 2019

The algorithm uses PEDCC (Predefined Evenly-Distributed Class Centroids) as the clustering centers, which ensures the inter-class distance of latent features is maximal, and adds data distribution constraint, data augmentation constraint, auto-encoder reconstruction constraint and Sobel smooth constraint to improve the clustering performance.

Graph Degree Linkage: Agglomerative Clustering on a Directed Graph

waynezhanghk/gactoolbox 25 Aug 2012

We explore the different roles of two fundamental concepts in graph theory, indegree and outdegree, in the context of clustering.