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Image Clustering

24 papers with code · Computer Vision

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Symmetric Nonnegative Matrix Factorization for Graph Clustering

SDM 2012 benedekrozemberczki/karateclub

Unlike NMF, however, SymNMF is based on a similarity measure between data points, and factorizes a symmetric matrix containing pairwise similarity values (not necessarily nonnegative).

GRAPH CLUSTERING IMAGE CLUSTERING SPECTRAL GRAPH CLUSTERING

Joint Unsupervised Learning of Deep Representations and Image Clusters

CVPR 2016 jwyang/JULE-Torch

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

IMAGE CLUSTERING REPRESENTATION LEARNING

FineGAN: Unsupervised Hierarchical Disentanglement for Fine-Grained Object Generation and Discovery

CVPR 2019 kkanshul/finegan

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.

CONDITIONAL IMAGE GENERATION FINE-GRAINED VISUAL CATEGORIZATION IMAGE CLUSTERING

Deep Subspace Clustering Networks

NeurIPS 2017 panji1990/Deep-subspace-clustering-networks

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

IMAGE CLUSTERING

Sparse Subspace Clustering: Algorithm, Theory, and Applications

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

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.

IMAGE CLUSTERING MOTION SEGMENTATION

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

16 Aug 2019rymc/n2d

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.

IMAGE CLUSTERING REPRESENTATION LEARNING TIME SERIES TIME SERIES CLUSTERING

Deep Comprehensive Correlation Mining for Image Clustering

ICCV 2019 Cory-M/DCCM

Recent developed deep unsupervised methods allow us to jointly learn representation and cluster unlabelled data.

IMAGE CLUSTERING

Deep Multimodal Subspace Clustering Networks

17 Apr 2018mahdiabavisani/Deep-multimodal-subspace-clustering-networks

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.

MULTI-MODAL SUBSPACE CLUSTERING MULTIVIEW LEARNING MULTI-VIEW SUBSPACE CLUSTERING

Deep clustering: On the link between discriminative models and K-means

9 Oct 2018MOhammedJAbi/SoftKMeans

Typically, they use multinomial logistic regression posteriors and parameter regularization, as is very common in supervised learning.

IMAGE CLUSTERING