Deep Variational Clustering Framework for Self-labeling Large-scale Medical Images This is the official PyTorch implementation of the DVC paper:
@Article{
}
conda install pytorch torchvision torchaudio cudatoolkit=11.1 -c pytorch -c conda-forge
- MNIST
- Skin Cancer
- REFUGE-2
To Train DVC run cvae_idec.py
python cvae_idec.py --batch_size 256 --lr 0.001
optional arguments:
--lr Learnig rate
--n_clusters Number of cluster
--n_z Size of embbeding layer
--batch_size Number of images in each mini-batch [default value is 512]
--dataset-name Name of the dataset (e.g., mnist, skin, retina)
--pretrain_path Path of pretrained model (e.g., "saved_models/VAE/cvae_cifar10.pkl")
--early_patience Number of epochs before triggering the early stopping.
--gamma Coefficient of clustering loss
--update_interval Specify the update interval of target distribution