Code for reproducing experiments in "Improved Training of Wasserstein GANs"

Last update: Jan 01, 2023

Related tags

Deep Learning improved_wgan_training

Overview

Improved Training of Wasserstein GANs

Code for reproducing experiments in "Improved Training of Wasserstein GANs".

Prerequisites

Python, NumPy, TensorFlow, SciPy, Matplotlib
A recent NVIDIA GPU

Models

Configuration for all models is specified in a list of constants at the top of the file. Two models should work "out of the box":

python gan_toy.py: Toy datasets (8 Gaussians, 25 Gaussians, Swiss Roll).
python gan_mnist.py: MNIST

For the other models, edit the file to specify the path to the dataset in DATA_DIR before running. Each model's dataset is publicly available; the download URL is in the file.

python gan_64x64.py: 64x64 architectures (this code trains on ImageNet instead of LSUN bedrooms in the paper)
python gan_language.py: Character-level language model
python gan_cifar.py: CIFAR-10

Owner

Ishaan Gulrajani

GitHub Repository

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Code for reproducing experiments in "Improved Training of Wasserstein GANs"

Related tags

Overview

Improved Training of Wasserstein GANs

Prerequisites

Models

Owner

Ishaan Gulrajani

StudioGAN is a Pytorch library providing implementations of representative Generative Adversarial Networks (GANs) for conditional/unconditional image generation.

A deep learning framework for historical document image analysis

Code for You Only Cut Once: Boosting Data Augmentation with a Single Cut

toroidal - a lightweight transformer library for PyTorch

🐸STT integration examples

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Official PyTorch implementation of PS-KD

The code for our NeurIPS 2021 paper "Kernelized Heterogeneous Risk Minimization".

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