vivonasg / capsgan

Unsupervised representation learning with CapsNet based Generative Adversarial Networks

CapsGAN

Unsupervised representation learning with CapsNet based Generative Adversarial Networks

Description

Coalesces Wassertein Distance algorithm based generative adversarial networks with deep convolutinal network based discriminator replaced by CapsNet architecture.

Prerequisits and Requirements

• Python > 3.6
• PyTorch
• TorchVision
• TorchNet
• TQDM
• Visdom [optional]

** For training, an NVIDIA TITAN XP GPU was used. Using CUDA with GPU is strongly recommended. CPU is supported but speed of training will be very slow.

Datasets

• MNIST
• CIFAR-10

Usage

Utilizes FAIR's visdom as visulization tool. If you'd like to visualize the test and train results, run with visualize args.

$ sudo python3 -m visdom.server &
$ python3 main.py --visualize --cuda

Else, simply run:

$ python3 main.py --dataset cifar10 --dataroot ./data --cuda --niter [NUM_EPOCHS] --

To run with MLP as G or D, run:

$ python3 main.py --dataset cifar10 --dataroot ./data --cuda --experiment {Name} --mlp_G --ngf 512

Contact

Please send an email to raeidsaqur[at]cs[dot]toronto[dot]edu for questions, PRs etc.

*** Note: Improved ReadMe is in the works!