Contrastive Clustering |
- |
AAAI2021 |
Pytorch |
Deep Transformation-Invariant Clustering |
DTI |
NeurIPS2020 |
Pytorch |
Unsupervised Learning of Visual Features by Contrasting Cluster Assignments |
SwAV |
NeurIPS2020 |
PyTorch |
Adversarial Learning for Robust Deep Clustering |
ALRDC |
NeurIPS 2020 |
Keras |
Learning to Cluster under Domain Shift |
ACIDS |
ECCV 2020 |
Pytorch |
Deep Semantic Clustering by Partition Confidence Maximisation |
PICA |
CVPR 2020 |
Pytorch |
Online Deep Clustering for Unsupervised Representation Learning |
ODC |
CVPR 2020 |
Python |
Improving k-Means Clustering Performance with Disentangled Internal Representations |
Annealing SNNL |
IJCNN 2020 |
PyTorch |
N2D: (Not Too) Deep Clustering via Clustering the Local Manifold of an Autoencoded Embedding. |
N2D |
ICPR 2020 |
TensorFlow |
Optimal Sampling and Clustering in the Stochastic Block Model |
|
NeurIPS 2019 |
Python |
Selective Sampling-based Scalable Sparse Subspace Clustering |
S5C |
NeurIPS 2019 |
MATLAB |
Unsupervised Clustering using Pseudo-semi-supervised Learning |
Kingdra |
ICLR 2020 |
Keras |
Spectral Clustering with Graph Neural Networks for Graph Pooling |
- |
ICML 2020 |
TensorFlow |
Self-labelling via simultaneous clustering and representation learning |
SeLa |
ICLR2020 |
Pytorch |
Deep clustering: On the link between discriminative models and K-means |
SoftK-means |
TPAMI 2020 |
Python |
Image Clustering via Deep Embedded Dimensionality Reduction and Probability-Based Triplet Loss |
DERC |
TIP2020 |
TensorFlow |
Learning Latent Superstructures in Variational Autoencoders for Deep Multidimensional Clustering |
LTVAE |
ICLR 2019 |
Pytorch |
Balanced Self-Paced Learning for Generative Adversarial Clustering Network |
ClusterGAN |
CVPR2019 |
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Deep Embedded Clustering with Data Augmentation |
DEC-DA |
ACML 2018 |
TensorFlow |
Adaptive Self-paced Deep Clustering with Data Augmentation |
ASPC-DA |
TKDE 2019 |
TensorFlow |
Structural Deep Clustering Network |
SDCN |
WWW 2020 |
Pytorch |
Learning to Cluster Faces on an Affinity Graph |
LTC |
CVPR 2019 |
Pytorch |
Video Face Clustering with Unknown Number of Clusters |
BCL |
ICCV 2019 |
Pytorch |
ClusterSLAM: A SLAM Backend for Simultaneous Rigid Body |
ClusterSLAM |
ICCV 2019 |
|
Deep Clustering by Gaussian Mixture Variational Autoencoders with Graph Embedding |
DGG |
ICCV 2019 |
Coming Soon |
Deep Comprehensive Correlation Mining for Image Clustering |
DCCM |
ICCV 2019 |
Pytorch |
Invariant Information Clustering for Unsupervised Image Classification and Segmentation |
IIC |
ICCV 2019 |
Pytorch |
Subspace Structure-aware Spectral Clustering for Robust Subspace Clustering |
|
ICCV 2019 |
|
Is an Affine Constraint Needed for Affine Subspace Clustering? |
|
ICCV 2019 |
|
Deep Spectral Clustering using Dual Autoencoder Network |
|
ICCV 2019 |
Tensorflow |
Learning to Discover Novel Visual Categories via Deep Transfer Clustering |
DTC |
ICCV 2019 |
Pytorch |
Reciprocal Multi-Layer Subspace Learning for Multi-View Clustering |
RMSL |
ICCV 2019 |
|
GEMSEC: Graph Embedding with Self Clustering |
GEMSEC |
ASONAM 2019 |
TensorFlow |
ClusterGAN: Latent Space Clustering in Generative Adversarial Networks |
ClusterGAN |
AAAI 2019 |
TensorFlow |
Adversarial Graph Embedding for Ensemble Clustering |
AGAE |
IJCAI 2019 |
|
A Hybrid Autoencoder Network for Unsupervised Image Clustering |
|
Algorithms 2019 |
|
A Deep Clustering Algorithm based on Gaussian Mixture Model |
|
Journal of Physics: Conference Series 2019 |
|
Deep Clustering for Unsupervised Learning of Visual Features |
DeepCluster |
ECCV 2018 |
Pytorch |
Deep Clustering with Convolutional Autoencoders |
DCEC |
ICONIP 2018 |
Keras |
SpectralNet: Spectral Clustering Using Deep Neural Networks |
SpectralNet |
ICLR 2018 |
TensorFlow |
Subspace clustering using a low-rank constrained autoencoder |
LRAE |
Information Science 2018 |
|
Unsupervised Multi-Manifold Clustering by Learning Deep Representation |
|
AAAI 2017 Workshop |
|
Deep Discriminative Latent Space for Clustering |
|
NeurIPS 2017 |
|
Towards K-means-friendly Spaces: Simultaneous Deep Learning and Clustering |
DCN |
PMLR 2017 |
Theano |
Deep Unsupervised Clustering With Gaussian Mixture Variational AutoEncoders |
GMVAE |
ICLR 2017 |
Lua |
Is Simple Better?: Revisiting Simple Generative Models for Unsupervised Clustering |
|
NeurIPS 2017 Workshop |
Pytorch |
Improved Deep Embedded Clustering with Local Structure Preservation |
IDEC |
IJCAI 2017 |
Keras,Pytorch |
Deep Clustering via joint convolutional autoencoder embedding and relative entropy minimization |
DEPICT |
ICCV 2017 |
Theano |
Deep Adaptive Image Clustering |
|
ICCV 2017 |
|
Variational Deep Embedding: An Unsupervised and Generative Approach to Clustering |
VaDE |
IJCAI 2017 |
Keras |
Deep Subspace Clustering Networks |
DSC-Nets |
NeurIPS 2017 |
TensorFlow |
Unsupervised Deep Embedding for Clustering Analysis |
DEC |
ICML 2016 |
Caffe TensorFlow |
Joint Unsupervised Learning of Deep Representations and Image Clustering |
JULE |
CVPR 2016 |
Torch |
Clustering Meets Implicit Generative Models |
|
ICLR 2019 workshop |
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CNN-Based Joint Clustering and Representation Learning with Feature Drift Compensation for Large-Scale Image Data |
|
IEEE Transactions on Multimedia 2017 |
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Learning Discrete Representations via Information Maximizing Self-Augmented Training |
IMSAT |
ICML 2017 |
code |
Deep Embedding Network for Clustering |
DEN |
ICPR 2014 |
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Auto-encoder Based Data Clustering |
ABDC |
CIARP 2013 |
Pytorch |
Learning Deep Representations for Graph Clustering |
|
AAAI 2014 |
python |
Discriminative Clustering by Regularized Information Maximization |
RIM |
NeurIPS 2010 |
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