Useful resources

[srinathdama]

1. Cluster GAN

Paper: https://arxiv.org/pdf/1809.03627.pdf

code (authors, Tensorflow): https://github.com/sudiptodip15/ClusterGAN

code (PyTorch): https://github.com/zhampel/clusterGAN

issues
MNIST Dataset:
For old versions of TorchVision (<= 1.0.1*)
wget -O MNIST.tar.gz https://activeeon-public.s3.eu-west-2.amazonaws.com/datasets/MNIST.old.tar.gz
tar -zxvf MNIST.tar.gz

conda commands:

conda create --name clusterGANpytorch python=3.6.8 -y

conda activate clusterGANpytorch

install packages

conda install --file requirements.txt
or
pip install -r requirements.txt
or
python setup.py install

2. VAEs for clustering

Paper 1: Deep Unsupervised Clustering with Gaussian Mixture Variational Autoencoders (https://arxiv.org/abs/1611.02648)

code (authors, lua): https://github.com/Nat-D/GMVAE
code (pytorch): https://github.com/SiyuWang15/GMVAE-pytorch

Paper 2: Variational Deep Embedding : A Generative Approach to Clustering (https://arxiv.org/pdf/1611.05148.pdf)

code (keras): https://github.com/slim1017/VaDE

Paper 3: Deep Clustering by Gaussian Mixture Variational Autoencoders with Graph Embedding (https://openaccess.thecvf.com/content_ICCV_2019/papers/Yang_Deep_Clustering_by_Gaussian_Mixture_Variational_Autoencoders_With_Graph_Embedding_ICCV_2019_paper.pdf)

code (PyTorch): https://github.com/ngoc-nguyen-0/DGG

Paper 4: LEARNING LATENT SUPERSTRUCTURES IN VARIATIONAL AUTOENCODERS FOR DEEP MULTIDIMENSIONAL CLUSTERING (https://openreview.net/pdf?id=SJgNwi09Km)

code (PyTorch): https://github.com/eelxpeng/ltvae-release

Miscellaneous:

https://debuggercafe.com/category/autoencoders/

https://debuggercafe.com/convolutional-variational-autoencoder-in-pytorch-on-mnist-dataset/

https://github.com/dragen1860/pytorch-mnist-vae

Paper 1: Deep Continuous Clustering (https://arxiv.org/pdf/1803.01449.pdf)

code (PyTorch): https://github.com/shahsohil/DCC

Sk-learn baseline references:

1. https://jakevdp.github.io/PythonDataScienceHandbook/05.12-gaussian-mixtures.html

2. https://towardsdatascience.com/unsupervised-learning-of-gaussian-mixture-models-on-a-selu-auto-encoder-not-another-mnist-11fceccc227e

• code: https://github.com/abriosi/gmm-mml

3. https://github.com/aksharas28/Unsupervised-Learning--Clustering-Analysis-on-Fashion-MNIST-Data

4. https://medium.com/sfu-cspmp/distilling-gaussian-mixture-models-701fa9546d9

5. http://www.oranlooney.com/post/ml-from-scratch-part-5-gmm/

6. https://pythonmachinelearning.pro/clustering-with-gaussian-mixture-models/

Datasets:

• https://scikit-learn.org/0.19/datasets/mldata.html

Deep Clustering repos compilation: https://github.com/zhoushengisnoob/DeepClustering