generative_zoo

generative_zoo is a repository that provides working implementations of some generative models in PyTorch.

Available Implementations

Name of Generative Model	Brief Description	References (if any)
Multilayer Perceptron GAN (MLPGAN)	Generative Adversarial Network with MLP Generator Network and Discriminator Network	Ian Goodfellow et al.
Deep Convolutional GAN (DCGAN)	Generative Adversarial Network with Deep Convolutional Generator and Discriminator Network	Alec Radford et al.
Variational Autoencoder (VAE)	Better performing Autoencoder with a single layer Encoder and Decoder Network	Kingma et al.
Wasserstein GAN (WGAN)	Generative Adversarial Network with a different method of training	Martin Arjovsky et al.
α GAN	Generative Adversarial Network combined with an Auto-Encoder and a different training strategy	Mihaela Rosca and Balaji Lakshminarayanan et al.

Broken Implementations

• The code for VAEGAN does not generate good images. If you would like to contribute to the repo, please help solve this issue. Thanks!!
• The code for Improved WGAN provides bad images and the training is extremely unstable. If you would like to contribute to the repo, please help solve this issue. Thanks!!

Datasets

Name of Dataset	Brief Description	References (if any)
MNIST	Digit Recognition dataset	Yann LeCun et al.
CIFAR10	Color Image Recoginition dataset	Alex Krizhevsky
LSUN	Large Scene Understanding	Fisher Yu et al.
CUB200	Birds Dataset	Wellinder et al.
FASHION-MNIST	Clothes dataset with similar characteristics as MNIST	Zalando Research

Requirements

• Python 3.x
• PyTorch 0.2 or better (If you want to test other models except Improved WGAN : then you can use PyTorch 0.10 or better)
• Torchvision

File Structure

• Architectures contains dataset specific architectures (currently only for MNIST and CIFAR10) and a Generic Architecture generator
• Modules contains the key modules of the GANs
• Mains contains the sample Main files written for the GANs
• Utilities contains some utility files and CUB200 and FASHION-MNIST dataset loaders
• Examples contain some GIFs that show the variation in the improving quality of generated images with time

Usage of the implementations

• Clone this repository using

git clone https://github.com/DL-IT/generative_zoo.git

• From the directory you have cloned into, you will be able to use the code. Just perform

import generative_zoo

• There are multiple submodules such as Architectures, Modules and Utilities.

  • In Architectures, you can take a data-specific architecture for MNIST and CIFAR10 and a generic architecture which can be used for any square image in general. You need to use it using the below code:

  generator = generative_zoo.Architectures.<name>.Generator(*args)
  discriminator = generative_zoo.Architectures.<name>.Discriminator(*args)

  Here <name> can be CIFAR10, MNIST or Generic.

  • In Modules, you can take specific types of GANs which are listed above. This can be invoked using

  my_GAN = generative_zoo.Modules.<name>(*args)

  Here <name> can be any one of the above GAN types.

  • In Utilities, you can have some basic utilities and dataset providers. Especially, data_utilities inside Utilities provides data of required size and certain other specifications.

• In addition to all this, there are sample main files to give you an idea as to how the code works in all, available in Mains. This has to be accessed externally.

Some generations

MNIST	CIFAR10