Computer Vision
 
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Image Clustering

118 papers with code • 55 benchmarks • 44 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)

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Most implemented papers

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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.

259
Paper
Code

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.

145
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Unsupervised Deep Embedding for Clustering Analysis

piiswrong/dec • • 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.

23
Paper
Code

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.

9
Paper
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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.

6
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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.

5
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Code

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.

5
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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.

4
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Joint Unsupervised Learning of Deep Representations and Image Clusters

jwyang/joint-unsupervised-learning • • CVPR 2016

In this paper, we propose a recurrent framework for Joint Unsupervised LEarning (JULE) of deep representations and image clusters.

3
Paper
Code

Deep Subspace Clustering Networks

panji1990/Deep-subspace-clustering-networks • • NeurIPS 2017

We present a novel deep neural network architecture for unsupervised subspace clustering.

3
Paper
Code
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