zhoushengisnoob / deepclustering

Methods and Implements of Deep Clustering

[:bell: News! :bell: ] We have released a new survey paper based on this repository, with a new perspective of existing deep clustering methods! We are looking forward to any comments or discussions on this topic :)

Data type specific deep clustering: We also provide some interesting data type specific deep clustering:

• Deep Graph Clustering

Paper List

Survey Paper	Conference
🚩 A Comprehensive Survey on Deep Clustering: Taxonomy, Challenges, and Future Directions	arXiv 2022
Deep Clustering: A Comprehensive Survey	arXiv 2022
A Survey of Clustering With Deep Learning: From the Perspective of Network Architecture	IEEE ACCESS 2018
Clustering with Deep Learning: Taxonomy and New Methods	arXiv 2018
Unsupervised clustering for deep learning: A tutorial survey	APH 2018

Pre-print Paper	Method	Conference	Code
C3: Cross-instance guided Contrastive Clustering	C3	Arxiv 2022	Pytorch
Learning Statistical Representation with Joint Deep Embedded Clustering	StatDEC	arXiv 2021	-
Cluster Analysis with Deep Embeddings and Contrastive Learning	-	arXiv 2021	-
Deep Clustering with Self-supervision using Pairwise Data Similarities	DCSS	TechRxiv 2021	Pytorch
Deep clustering by semantic contrastive learning	SCL	arXiv 2021	-
Doubly contrastive deep clustering	DCDC	arXiv 2021	Pytorch
DHOG: Deep Hierarchical Object Grouping	DHOG	arXiv 2020	-
Deep Robust Clustering by Contrastive Learning	DRC	arXiv 2020	-
Un-Mix: Rethinking Image Mixture for Unsupervised Visual Representation Learning	Un-Mix	arXiv 2020	Pytorch
Differentiable Deep Clustering with Cluster Size Constraints	-	arXiv 2019	-
Deep Continuous Clustering	DCC	arXiv 2018	Pytorch
Clustering-driven Deep Embedding with Pairwise Constraints	CPAC	arXiv 2018	Pytorch
Deep Temporal Clustering : Fully Unsupervised Learning of Time-Domain Features	DTC	arXiv 2018	Keras
Graph Clustering with Dynamic Embedding	GRACE	arXiv 2017	-
Deep Unsupervised Clustering using Mixture of Autoencoders	MIXAE	arXiv 2017	-
Discriminatively Boosted Image Clustering with Fully Convolutional Auto-Encoders	DBC	arXiv 2017	-
Deep Clustering Network	DCN	arXiv 2016	Theano

Paper	Method	Conference	Code
A Clustering Framework for Unsupervised and Semi-supervised New Intent Discovery	USNID	IEEE TKDE 2023	Pytorch
Deep Multiview Clustering by Contrasting Cluster Assignments	CVCL	ICCV 2023	Pytorch
Stable Cluster Discrimination for Deep Clustering	SeCu	ICCV 2023	-
Cross-view Topology Based Consistent and Complementary Information for Deep Multi-view Clustering	CTCC	ICCV 2023	-
Deep Multi-view Subspace Clustering with Anchor Graph	DMCAG	IJCAI 2023	-
Incomplete Multi-view Clustering via Prototype-based Imputation	ProImp	IJCAI 2023	-
CONGREGATE: Contrastive Graph Clustering in Curvature Spaces	CONGREGATE	IJCAI 2023	Pytorch
Multi-level Graph Contrastive Prototypical Clustering	MLG-CPC	IJCAI 2023	-
Dink-Net: Neural Clustering on Large Graphs	Dink-Net	ICML 2023	Pytorch
Cluster-Guided Contrastive Graph Clustering Network	CCGC	AAAI 2023	Pytorch
Hard Sample Aware Network for Contrastive Deep Graph Clustering	HSAN	AAAI 2023	Pytorch
Dual Mutual Information Constraints for Discriminative Clustering	DMICC	AAAI 2023	Pytorch
Self-Supervised Graph Attention Networks for Deep Weighted Multi-View Clustering	SGDMC	AAAI 2023	-
Scalable Attributed-Graph Subspace Clustering	SAGSC	AAAI 2023	TensorFlow
Semantic-Enhanced Image Clustering	SIC	AAAI 2023	-
GLCC: A General Framework for Graph-Level Clustering	GLCC	AAAI 2023	-
Highly Confident Local Structure Based Consensus Graph Learning for Incomplete Multi-View Clustering	HCLS_CGL	CVPR 2023	-
Deep Incomplete Multi-view Clustering with Cross-view Partial Sample and Prototype Alignment	IMVC	CVPR 2023	-
On the Effects of Self-supervision and Contrastive Alignment in Deep Multi-view Clustering	DeepMVC	CVPR 2023	Pytorch
DivClust: Controlling Diversity in Deep Clustering	DivClust	CVPR 2023	Pytorch
SPICE: Semantic Pseudo-labeling for Image Clustering	SPICE	TIP 2022	Pytorch
Generalised Mutual Information for Discriminative Clustering	GEMINI	NeurIPS 2022	-
Self-supervised Heterogeneous Graph Pre-training Based on Structural Clustering	SHGP	NeurIPS 2022	Pytorch
Learning Representation for Clustering via Prototype Scattering and Positive Sampling	ProPos	TPAMI 2022	Pytorch
Dual Contrastive Prediction for Incomplete Multi-view Representation Learning	DCP	TPAMI 2022	Pytorch
GOCA: Guided Online Cluster Assignment for Self-supervised Video Representation Learning	GOCA	ECCV 2022	Pytorch
Fine-Grained Fashion Representation Learning by Online Deep Clustering	MODC	ECCV 2022	-
Embedding Contrastive Unsupervised Features to Cluster In- and Out-of-distribution Noise in Corrupted Image Datasets	SNCF	ECCV 2022	Pytorch
On Mitigating Hard Clusters for Face Clustering	-	ECCV 2022	Pytorch
Deep Safe Incomplete Multi-view Clustering: Theorem and Algorithm	DSIMVC	ICML 2022	Pytorch
Locally Normalized Soft Contrastive Clustering for Compact Clusters	LNSCC	IJCAI 2022	-
Contrastive Multi-view Hyperbolic Hierarchical Clustering	CMHHC	IJCAI 2022	-
EMGC$^2$F: Effcient Multi-view Graph Clustering with Comprehensive Fusion	EMGC$^2$F	IJCAI 2022	-
Efficient Orthogonal Multi-view Subspace Clustering	OMSC	KDD 2022	MATLAB
Clustering with Fair-Center Representation: Parameterized Approximation Algorithms and Heuristics	-	KDD 2022	-
DeepDPM: Deep Clustering With an Unknown Number of Clusters	DeepDPM	CVPR 2022	Pytorch
Unsupervised Action Segmentation by Joint Representation Learning and Online Clustering	-	CVPR 2022	-
Efficient Deep Embedded Subspace Clustering	EDESC	CVPR 2022	Pytorch
SLIC: Self-Supervised Learning With Iterative Clustering for Human Action Videos	SLIC	CVPR 2022	Pytorch
MPC: Multi-View Probabilistic Clustering	MPC	CVPR 2022	-
Deep Safe Multi-View Clustering: Reducing the Risk of Clustering Performance Degradation Caused by View Increase	DSMVC	CVPR 2022	-
Discriminative Similarity for Data Clustering	CDS	ICLR 2022	-
A Deep Variational Approach to Clustering Survival Data	VaDeSC	ICLR 2022	TensorFlow
Contrastive Fine-grained Class Clustering via Generative Adversarial Networks	C3-GAN	ICLR 2022	Pytorch
Deep Clustering of Text Representations for Supervision-Free Probing of Syntax	SyntDEC	AAAI 2022	-
Deep Graph Clustering via Dual Correlation Reduction	DCRN	AAAI 2022	Pytorch
Top-Down Deep Clustering with Multi-generator GANs	HC-MGAN	AAAI 2022	Pytorch
Neural generative model for clustering by separating particularity and commonality	DGC	Information Sciences 2022	-
Information Maximization Clustering via Multi-View Self-Labelling	IMC-SwAV	Knowledge-Based Systems 2022	Pytorch
Sign prediction in sparse social networks using clustering and collaborative filtering	-	TJSC 2022	-
You Never Cluster Alone	TCC	NeurIPS 2021	-
Multi-Facet Clustering Variational Autoencoders	MFCVAE	NeurIPS 2021	Pytorch
Multi-view Contrastive Graph Clustering	MCGC	NeurIPS 2021	Python
Graph Contrastive Clustering	GCC	ICCV 2021	Pytorch
One-pass Multi-view Clustering for Large-scale Data	OPMC	ICCV 2021	Matlab
Multi-VAE: Learning Disentangled View-common and View-peculiar Visual Representations for Multi-view Clustering	Multi-VAE	ICCV 2021	Pytorch
Learn to Cluster Faces via Pairwise Classification	-	ICCV 2021	-
Multimodal Clustering Networks for Self-supervised Learning from Unlabeled Videos	MCN	ICCV 2021	Pytorch
Clustering by Maximizing Mutual Information Across Views	CRLC	ICCV 2021	-
End-to-End Robust Joint Unsupervised Image Alignment and Clustering	Jim-Net	ICCV 2021	-
Learning Hierarchical Graph Neural Networks for Image Clustering	Hi-LANDER	ICCV 2021	Pytorch
Deep Descriptive Clustering	DDC	IJCAI 2021	-
Details (Don't) Matter: Isolating Cluster Information in Deep Embedded Spaces	ACe/DeC	IJCAI 2021	-
Graph Debiased Contrastive Learning with Joint Representation Clustering	GDCL	IJCAI 2021	Pytorch
Unsupervised Feature Learning by Cross-Level Instance-Group Discrimination	CLD	CVPR 2021	Pytorch
Nearest Neighbor Matching for Deep Clustering	NNM	CVPR 2021	Pytorch
Jigsaw Clustering for Unsupervised Visual Representation Learning	JigsawClustering	CVPR 2021	Pytorch
COMPLETER: Incomplete Multi-view Clustering via Contrastive Prediction	COMPLETER	CVPR 2021	Pytorch
Reconsidering Representation Alignment for Multi-view Clustering	SiMVC & CoMVC	CVPR 2021	Pytorch
Double Low-rank Representation with Projection Distance Penalty for Clustering	DLRRPD	CVPR 2021	Matlab
Improving Unsupervised Image Clustering With Robust Learning	RUC	CVPR 2021	Pytorch
Learning a Self-Expressive Network for Subspace Clustering	SENet	CVPR 2021	Pytorch
Clusformer: A Transformer Based Clustering Approach to Unsupervised Large-Scale Face and Visual Landmark Recognition	Clusformer	CVPR 2021	-
Cluster-wise Hierarchical Generative Model for Deep Amortized Clustering	CHiGac	CVPR 2021	-
Refining Pseudo Labels with Clustering Consensus over Generations for Unsupervised Object Re-identification	RLCC	CVPR 2021	-
Clustering-friendly Representation Learning via Instance Discrimination and Feature Decorrelation	IDFD	ICLR 2021	Pytorch
MiCE: Mixture of Contrastive Experts for Unsupervised Image Clustering	MiCE	ICLR 2021	Pytorch
Discovering New Intents with Deep Aligned Clustering	DeepAligned	AAAI 2021	Pytorch
Contrastive Clustering	CC	AAAI 2021	Pytorch
Interpretable Clustering on Dynamic Graphs with Recurrent Graph Neural Networks	RNNGCN	AAAI 2021	Pytorch
LRSC: Learning Representations for Subspace Clustering	LRSC	AAAI 2021	-
Deep Fusion Clustering Network	DFCN	AAAI 2021	Pytorch
Variational Deep Embedding Clustering by Augmented Mutual Information Maximization	VCAMI	ICPR 2021	-
Supporting Clustering with Contrastive Learning	SCCL	NAACL 2021	Pytorch
Pseudo-Supervised Deep Subspace Clustering	PSSC	TIP 2021	TensorFlow
A hybrid approach for text document clustering using Jaya optimization algorithm	HJO-DC	ESWA 2021	-
Deep video action clustering via spatio-temporal feature learning	DVAC	Neurocomputing 2021	-
A new clustering method for the diagnosis of CoVID19 using medical images	IGSA	Applied Intelligence 2021	-
A Decoder-Free Variational Deep Embedding for Unsupervised Clustering	DFVC	TNNLS 2021	-
Image clustering using an augmented generative adversarial network and information maximization	-	TNNLS 2021	TensorFlow
Learning the Precise Feature for Cluster Assignment	-	IEEE Trans Cybern 2021	TensorFlow
Deep Subspace Clustering with Data Augmentation	DSCwithDA	NeurIPS 2020	Pytorch
Deep Transformation-Invariant Clustering	DTI	NeurIPS 2020	Pytorch
Unsupervised Learning of Visual Features by Contrasting Cluster Assignments	SwAV	NeurIPS 2020	Pytorch
Adversarial Learning for Robust Deep Clustering	ALRDC	NeurIPS 2020	Keras
Self-supervised learning by cross-modal audio-video clustering	XDC	NeurIPS 2020	Pytorch
Mitigating Embedding and Class Assignment Mismatch in Unsupervised Image Classification	TSUC	ECCV 2020	Pytorch
GATCluster: Self-Supervised Gaussian-Attention Network for Image Clustering	GATCluster	ECCV 2020	Pytorch
Deep Image Clustering with Category-Style Representation	DCCS	ECCV 2020	Pytorch
MPCC: Matching Priors and Conditionals for Clustering	MPCC	ECCV 2020	Pytorch
SCAN: Learning to Classify Images without Labels	SCAN	ECCV 2020	Pytorch
Learning to Cluster under Domain Shift	ACIDS	ECCV 2020	Pytorch
Multi-View Attribute Graph Convolution Networks for Clustering	MAGCN	IJCAI 2020	-
CDIMC-net: Cognitive Deep Incomplete Multi-view Clustering Network	CDIMC-net	IJCAI 2020	-
Spectral Clustering with Graph Neural Networks for Graph Pooling	-	ICML 2020	TensorFlow
Variational Clustering: Leveraging Variational Autoencoders for Image Clustering	-	IJCNN 2020	-
Improving k-Means Clustering Performance with Disentangled Internal Representations	Annealing SNNL	IJCNN 2020	Pytorch
Unsupervised clustering through gaussian mixture variational autoencoder with non-reparameterized variational inference and std annealing	NVISA	IJCNN 2020	-
Learning to Cluster Faces via Confidence and Connectivity Estimation	LTC v2	CVPR 2020	Pytorch
Density-Aware Feature Embedding for Face Clustering	DA-Net	CVPR 2020	-
Deep Semantic Clustering by Partition Confidence Maximisation	PICA	CVPR 2020	Pytorch
Online Deep Clustering for Unsupervised Representation Learning	ODC	CVPR 2020	Pytorch
Multi-Scale Fusion Subspace Clustering Using Similarity Constraint	SC-MSFSC	CVPR 2020	-
Unsupervised Clustering using Pseudo-semi-supervised Learning	Kingdra	ICLR 2020	Keras
Self-labelling via Simultaneous Clustering and Representation Learning	SeLa	ICLR 2020	Pytorch
Structural Deep Clustering Network	SDCN	WWW 2020	Pytorch
Discovering New Intents via Constrained Deep Adaptive Clustering with Cluster Refinement	CDAC+	AAAI 2020	Pytorch
Unified Graph and Low-Rank Tensor Learning for Multi-View Clustering	UGLTL	AAAI 2020	-
Multi-View Clustering in Latent Embedding Space	MCLES	AAAI 2020	MATLAB
Hierarchically Clustered Representation Learning	HCRL	AAAI 2020	-
Adaptive Two-Dimensional Embedded Image Clustering	A2DEIC	AAAI 2020	-
Learning to cluster documents into workspaces using large scale activity logs	-	SIGKDD 2020	-
N2D: (Not Too) Deep Clustering via Clustering the Local Manifold of an Autoencoded Embedding	N2D	ICPR 2020	TensorFlow
A text document clustering method based on weighted Bert model	-	ITNEC 2020	-
Deep clustering: On the link between discriminative models and K-means	SoftK-means	TPAMI 2020	Theano
Efficient and Effective Regularized Incomplete Multi-View Clustering	EE-IMVC	TPAMI 2020	-
Adversarial Deep Embedded Clustering: on a better trade-off between Feature Randomness and Feature Drift	ADEC	TKDE 2020	-
Schain-iram: An efficient and effective semi-supervised clustering algorithm for attributed heterogeneous information networks	SCHAIN-IRAM	TKDE 2020	-
Image Clustering via Deep Embedded Dimensionality Reduction and Probability-Based Triplet Loss	DERC	TIP 2020	TensorFlow
Deep Clustering with a Dynamic Autoencoder: From Reconstruction Towards Centroids Construction	DynAE	Neural Networks 2020	TensorFlow
Spectral Clustering via Ensemble Deep Autoencoder Learning (SC-EDAE)	SC-EDAE	PR 2020	-
Cross multi-type objects clustering in attributed heterogeneous information network	CMOC-AHIN	KBS 2020	-
Iterative transfer learning with neural network for clustering and cell type classification in single-cell RNA-seq analysis	ItClust	Nature machine intelligence 2020	Keras
Optimal Sampling and Clustering in the Stochastic Block Model	-	NeurIPS 2019	Python
Selective Sampling-based Scalable Sparse Subspace Clustering	S5C	NeurIPS 2019	MATLAB
GEMSEC: Graph Embedding with Self Clustering	GEMSEC	ASONAM 2019	TensorFlow
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	Pytorch
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	-
Adversarial Graph Embedding for Ensemble Clustering	AGAE	IJCAI 2019	-
Attributed Graph Clustering: A Deep Attentional Embedding Approach	DAEGC	IJCAI 2019	-
Neural Collaborative Subspace Clustering	-	ICML 2019	-
Self-Supervised Convolutional Subspace Clustering Network	S^2ConvSCN	CVPR 2019	-
Balanced Self-Paced Learning for Generative Adversarial Clustering Network	ClusterGAN	CVPR 2019	-
Linkage-based Face Clustering via Graph Convolution Network	L-GCN	CVPR 2019	Pytorch
Learning to Cluster Faces on an Affinity Graph	LTC	CVPR 2019	Pytorch
Learning Latent Superstructures in Variational Autoencoders for Deep Multidimensional Clustering	LTVAE	ICLR 2019	Pytorch
Clustering Meets Implicit Generative Models	-	ICLR 2019 workshop	-
ClusterGAN: Latent Space Clustering in Generative Adversarial Networks	ClusterGAN	AAAI 2019	TensorFlow
Cluster-gcn: An efficient algorithm for training deep and large graph convolutional networks	Cluster-GCN	SIGKDD 2019	TensorFlow
Adaptive Self-paced Deep Clustering with Data Augmentation	ASPC-DA	TKDE 2019	TensorFlow
Clustering with outlier removal	COR	TKDE 2019	-
Clustering single-cell RNA-seq data with a model-based deep learning approach	scDeepCluster	Nature Machine Intelligence 2019	Keras
A Hybrid Autoencoder Network for Unsupervised Image Clustering	-	Algorithms 2019	-
A Deep Clustering Algorithm based on Gaussian Mixture Model	-	JPCS 2019	-
Text document clustering using spectral clustering algorithm with particle swarm optimization	SCPSO	ESWA 2019	Python
Deep Clustering with Convolutional Autoencoders	DCEC	ICONIP 2018	Keras
RDEC: Integrating Regularization into Deep Embedded Clustering for Imbalanced Datasets	RDEC	ACML 2018	-
Deep Embedded Clustering with Data Augmentation	DEC-DA	ACML 2018	TensorFlow
Deep adversarial subspace clustering	DASC	CVPR 2018	-
Deep Clustering for Unsupervised Learning of Visual Features	DeepCluster	ECCV 2018	Pytorch
SpectralNet: Spectral Clustering Using Deep Neural Networks	SpectralNet	ICLR 2018	TensorFlow PyTorch
Mixture of GANs for Clustering	-	IJCAI 2018	-
Subspace Clustering using a Low-rank Constrained Autoencoder	LRAE	Information Science 2018	-
Deep Discriminative Latent Space for Clustering	-	NeurIPS 2017	-
Deep Subspace Clustering Networks	DSC-Nets	NeurIPS 2017	TensorFlow
Is Simple Better?: Revisiting Simple Generative Models for Unsupervised Clustering	-	NeurIPS 2017 Workshop	Pytorch
Deep Clustering via Joint Convolutional Autoencoder Embedding and Relative Entropy Minimization	DEPICT	ICCV 2017	Theano
Deep Adaptive Image Clustering	DAC	ICCV 2017	Keras
Improved Deep Embedded Clustering with Local Structure Preservation	IDEC	IJCAI 2017	Keras Pytorch
Variational Deep Embedding: An Unsupervised and Generative Approach to Clustering	VaDE	IJCAI 2017	Keras
Towards K-means-friendly Spaces: Simultaneous Deep Learning and Clustering	DCN	ICML 2017	Theano
Learning Discrete Representations via Information Maximizing Self-Augmented Training	IMSAT	ICML 2017	Python
Deep Unsupervised Clustering With Gaussian Mixture Variational AutoEncoders	GMVAE	ICLR 2017	Lua
Semi-supervised clustering in attributed heterogeneous information networks	SCHAIN	WWW 2017	MATLAB
Cascade Subspace Clustering	CSC	AAAI 2017	-
Unsupervised Multi-Manifold Clustering by Learning Deep Representation	DMC	AAAI 2017 Workshop	-
Combining structured node content and topology information for networked graph clustering	-	TKDD 2017	-
CNN-Based Joint Clustering and Representation Learning with Feature Drift Compensation for Large-Scale Image Data	-	TMM 2017	-
Robust continuous clustering	RCC	PNAS 2017	-
Unsupervised Deep Embedding for Clustering Analysis	DEC	ICML 2016	Caffe TensorFlow
Joint Unsupervised Learning of Deep Representations and Image Clusters	JULE	CVPR 2016	Torch
Deep subspace clustering with sparsity prior	PARTY	IJCAI 2016	-
CCCF: Improving collaborative filtering via scalable user-item co-clustering	CCCF	WSDM 2016	-
Deep Embedding Network for Clustering	DEN	ICPR 2014	-
Learning Deep Representations for Graph Clustering	-	AAAI 2014	Python
Auto-encoder Based Data Clustering	ABDC	CIARP 2013	Pytorch
Discriminative Clustering by Regularized Information Maximization	RIM	NeurIPS 2010	-

TIPS

If you find this repository useful to your research or work, it is really appreciate to star this repository.

deepclustering's Issues

VADE Model : CUDA error: CUBLAS_STATUS_EXECUTION_FAILED

I have an error while runing my model m it is related to CUDA

File /data/software/miniconda3/lib/python3.9/site-packages/torch/nn/modules/linear.py:114, in Linear.forward(self, input)
113 def forward(self, input: Tensor) -> Tensor:
--> 114 return F.linear(input, self.weight, self.bias)

RuntimeError: CUDA error: CUBLAS_STATUS_EXECUTION_FAILED when calling cublasLtMatmul( ltHandle, computeDesc.descriptor(), &alpha_val, mat1_ptr, Adesc.descriptor(), mat2_ptr, Bdesc.descriptor(), &beta_val, result_ptr, Cdesc.descriptor(), result_ptr, Cdesc.descriptor(), &heuristicResult.algo, workspace.data_ptr(), workspaceSize, at::cuda::getCurrentCUDAStream())

0%| | 0/10 [00:00<?, ?it/s]/data/software/miniconda3/lib/python3.9/site-packages/torch/optim/lr_scheduler.py:138: UserWarning: Detected call of lr_scheduler.step() before optimizer.step(). In PyTorch 1.1.0 and later, you should call them in the opposite order: optimizer.step() before lr_scheduler.step(). Failure to do this will result in PyTorch skipping the first value of the learning rate schedule. See more details at https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate
warnings.warn("Detected call of lr_scheduler.step() before optimizer.step(). "

Deep Clustering toolbox

Thanks for your awesome summary @zhoushengisnoob .

Are there any reports that implement multiple algorithms? like timm repo in ImageNet classification.

kernal killed- VaDE Model

i have an error in the kernal during runing the VaDE model
i faced this problem before , and i remember because i installed big pkgs.
How can I solve this? uninstall unneeded pkgs

Updated!

Improving Unsupervised Image Clustering With Robust Learning (arXiv'20) will appear in CVPR'21
Feel free to update accordingly.

Two important deep clustering papers

Thanks for considering these two papers:

1. Mrabah, Nairouz, et al. "Deep clustering with a Dynamic Autoencoder: From reconstruction towards centroids construction." Neural Networks 130 (2020): 206-228.

2. Mrabah, Nairouz, et al. "Adversarial deep embedded clustering: on a better trade-off between feature randomness and feature drift." IEEE Transactions on Knowledge and Data Engineering (2020).

Maybe a LeaderBoard is useful?

Can you set up a leaderboard and appeal the community to contribute to it? I think this will be helpful, because there have already been tons of papers now, which is kindly hard for beginners to choose some of them to read. A leaderboard with clearly performance gap between different methods may be of some help. Thanks!

Implementation of SeCu

The GitHub code of Stable Cluster Discrimination for Deep Clustering is given under this link by the author.

Deep clustering methods for categorical/tabular/mixed data

First, thank you for this brilliant synthesis of deep clustering algorithms!

I wonder, however, if slightly more emphasis could be given to the data types commonly seen in the clinical epidemiological/register-based context, such as categorical (including binary) data, as well as mixed categorical and continuous data, also often seen in such studies. The use of deep learning in this field is very promising, but it is not as well-compiled as deep clustering on naturally high-dimensional data such as images. In the literature, one may find recent papers focusing on this issue, e.g., the DeepTLF framework (https://doi.org/10.1007/s41060-022-00350-z) and recent breakthroughs should be highlighted. It would be very beneficial if a section could be added for these types of clustering methods in this repo.

Thanks again!

Daniil

tenserflow - error in tenserflow

---

TypeError Traceback (most recent call last)
Cell In[1], line 9
7 sys.path.insert(0, '/home/nfs/rksantini/Dec')
8 #sys.path.insert(0, '/content/drive/My Drive/Brain MRI Images for Brain Tumor Detection/')
----> 9 import tensorflow as tf
10 from tensorflow.keras.optimizers import SGD, Adam
11 import os

File /data/software/miniconda3/lib/python3.9/site-packages/tensorflow/init.py:37
34 import sys as _sys
35 import typing as _typing
---> 37 from tensorflow.python.tools import module_util as _module_util
38 from tensorflow.python.util.lazy_loader import LazyLoader as _LazyLoader
40 # Make sure code inside the TensorFlow codebase can use tf2.enabled() at import.

File /data/software/miniconda3/lib/python3.9/site-packages/tensorflow/python/init.py:37
29 # We aim to keep this file minimal and ideally remove completely.
30 # If you are adding a new file with @tf_export decorators,
31 # import it in modules_with_exports.py instead.
32
33 # go/tf-wildcard-import
34 # pylint: disable=wildcard-import,g-bad-import-order,g-import-not-at-top
36 from tensorflow.python import pywrap_tensorflow as _pywrap_tensorflow
---> 37 from tensorflow.python.eager import context
39 # pylint: enable=wildcard-import
40
41 # Bring in subpackages.
42 from tensorflow.python import data

File /data/software/miniconda3/lib/python3.9/site-packages/tensorflow/python/eager/context.py:28
25 from absl import logging
26 import numpy as np
---> 28 from tensorflow.core.framework import function_pb2
29 from tensorflow.core.protobuf import config_pb2
30 from tensorflow.core.protobuf import coordination_config_pb2

File /data/software/miniconda3/lib/python3.9/site-packages/tensorflow/core/framework/function_pb2.py:16
11 # @@protoc_insertion_point(imports)
13 _sym_db = _symbol_database.Default()
---> 16 from tensorflow.core.framework import attr_value_pb2 as tensorflow_dot_core_dot_framework_dot_attr_value_pb2
17 from tensorflow.core.framework import node_def_pb2 as tensorflow_dot_core_dot_framework_dot_node_def_pb2
18 from tensorflow.core.framework import op_def_pb2 as tensorflow_dot_core_dot_framework_dot_op_def_pb2

File /data/software/miniconda3/lib/python3.9/site-packages/tensorflow/core/framework/attr_value_pb2.py:16
11 # @@protoc_insertion_point(imports)
13 _sym_db = _symbol_database.Default()
---> 16 from tensorflow.core.framework import tensor_pb2 as tensorflow_dot_core_dot_framework_dot_tensor__pb2
17 from tensorflow.core.framework import tensor_shape_pb2 as tensorflow_dot_core_dot_framework_dot_tensor_shape_pb2
18 from tensorflow.core.framework import types_pb2 as tensorflow_dot_core_dot_framework_dot_types__pb2

File /data/software/miniconda3/lib/python3.9/site-packages/tensorflow/core/framework/tensor_pb2.py:16
11 # @@protoc_insertion_point(imports)
13 _sym_db = _symbol_database.Default()
---> 16 from tensorflow.core.framework import resource_handle_pb2 as tensorflow_dot_core_dot_framework_dot_resource_handle_pb2
17 from tensorflow.core.framework import tensor_shape_pb2 as tensorflow_dot_core_dot_framework_dot_tensor_shape_pb2
18 from tensorflow.core.framework import types_pb2 as tensorflow_dot_core_dot_framework_dot_types__pb2

File /data/software/miniconda3/lib/python3.9/site-packages/tensorflow/core/framework/resource_handle_pb2.py:16
11 # @@protoc_insertion_point(imports)
13 _sym_db = _symbol_database.Default()
---> 16 from tensorflow.core.framework import tensor_shape_pb2 as tensorflow_dot_core_dot_framework_dot_tensor_shape_pb2
17 from tensorflow.core.framework import types_pb2 as tensorflow_dot_core_dot_framework_dot_types__pb2
20 DESCRIPTOR = _descriptor.FileDescriptor(
21 name='tensorflow/core/framework/resource_handle.proto',
22 package='tensorflow',
(...)
26 ,
27 dependencies=[tensorflow_dot_core_dot_framework_dot_tensor_shapepb2.DESCRIPTOR,tensorflow_dot_core_dot_framework_dot_types_pb2.DESCRIPTOR,])

File /data/software/miniconda3/lib/python3.9/site-packages/tensorflow/core/framework/tensor_shape_pb2.py:36
13 _sym_db = _symbol_database.Default()
18 DESCRIPTOR = _descriptor.FileDescriptor(
19 name='tensorflow/core/framework/tensor_shape.proto',
20 package='tensorflow',
(...)
23 serialized_pb=_b('\n,tensorflow/core/framework/tensor_shape.proto\x12\ntensorflow"z\n\x10TensorShapeProto\x12-\n\x03\x64im\x18\x02 \x03(\x0b\x32 .tensorflow.TensorShapeProto.Dim\x12\x14\n\x0cunknown_rank\x18\x03 \x01(\x08\x1a!\n\x03\x44im\x12\x0c\n\x04size\x18\x01 \x01(\x03\x12\x0c\n\x04name\x18\x02 \x01(\tB\x87\x01\n\x18org.tensorflow.frameworkB\x11TensorShapeProtosP\x01ZSgithub.com/tensorflow/tensorflow/tensorflow/go/core/framework/tensor_shape_go_proto\xf8\x01\x01\x62\x06proto3')
24 )
29 _TENSORSHAPEPROTO_DIM = _descriptor.Descriptor(
30 name='Dim',
31 full_name='tensorflow.TensorShapeProto.Dim',
32 filename=None,
33 file=DESCRIPTOR,
34 containing_type=None,
35 fields=[
---> 36 _descriptor.FieldDescriptor(
37 name='size', full_name='tensorflow.TensorShapeProto.Dim.size', index=0,
38 number=1, type=3, cpp_type=2, label=1,
39 has_default_value=False, default_value=0,
40 message_type=None, enum_type=None, containing_type=None,
41 is_extension=False, extension_scope=None,
42 serialized_options=None, file=DESCRIPTOR),
43 _descriptor.FieldDescriptor(
44 name='name', full_name='tensorflow.TensorShapeProto.Dim.name', index=1,
45 number=2, type=9, cpp_type=9, label=1,
46 has_default_value=False, default_value=_b("").decode('utf-8'),
47 message_type=None, enum_type=None, containing_type=None,
48 is_extension=False, extension_scope=None,
49 serialized_options=None, file=DESCRIPTOR),
50 ],
51 extensions=[
52 ],
53 nested_types=[],
54 enum_types=[
55 ],
56 serialized_options=None,
57 is_extendable=False,
58 syntax='proto3',
59 extension_ranges=[],
60 oneofs=[
61 ],
62 serialized_start=149,
63 serialized_end=182,
64 )
66 _TENSORSHAPEPROTO = _descriptor.Descriptor(
67 name='TensorShapeProto',
68 full_name='tensorflow.TensorShapeProto',
(...)
100 serialized_end=182,
101 )
103 _TENSORSHAPEPROTO_DIM.containing_type = _TENSORSHAPEPROTO

File ~/.local/lib/python3.9/site-packages/google/protobuf/descriptor.py:561, in FieldDescriptor.new(cls, name, full_name, index, number, type, cpp_type, label, default_value, message_type, enum_type, containing_type, is_extension, extension_scope, options, serialized_options, has_default_value, containing_oneof, json_name, file, create_key)
555 def new(cls, name, full_name, index, number, type, cpp_type, label,
556 default_value, message_type, enum_type, containing_type,
557 is_extension, extension_scope, options=None,
558 serialized_options=None,
559 has_default_value=True, containing_oneof=None, json_name=None,
560 file=None, create_key=None): # pylint: disable=redefined-builtin
--> 561 _message.Message._CheckCalledFromGeneratedFile()
562 if is_extension:
563 return _message.default_pool.FindExtensionByName(full_name)

TypeError: Descriptors cannot not be created directly.
If this call came from a _pb2.py file, your generated code is out of date and must be regenerated with protoc >= 3.19.0.
If you cannot immediately regenerate your protos, some other possible workarounds are:

1. Downgrade the protobuf package to 3.20.x or lower.
2. Set PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION=python (but this will use pure-Python parsing and will be much slower).

PyTorch Implementation for SpectralNet (Shaham et al. ICLR 2018)

We have a new well-maintained PyTorch implementation for the above paper in the following link - SpectralNet - PyTorch

DeepClustering for CFD

Hello,
thanks for this repository and for the latest paper, very interesting!
I have a question: why, in your opinion, there are not so many applications, if any, of deep clustering, in the fluid dynamic field?

clustering of tabular data

hi there @zhunzhong07
currently i have a task that i want to solve dealing with clustering tabular data
are you available for freelancing?
if yes, then what is your email so i can share with you more information

Adding methods

Hi, Zhou. Awesome work. Well done.
Some more methods can be added to this list perhaps:
| Deep Embedded Clustering with Data Augmentation | DEC-DA | ACML 2018 | TensorFlow |
| Adaptive Self-paced Deep Clustering with Data Augmentation | ASPC-DA | TKDE 2019 | TensorFlow |

Additional, for DEC, there is a Keras implementation: https://github.com/XifengGuo/DEC-keras

Implementation of AHGFC

The code of Homophily-Related: Adaptive Hybrid Graph Filter for Multi-View Graph Clustering has been open-sourced at this link