What are the main features of mailmahee/pytorch-generative-adversarial-networks?

The main features of mailmahee/pytorch-generative-adversarial-networks are: Generative Models, Model Implementations, GANs, VAEs, and AEs.

What are some open-source alternatives to mailmahee/pytorch-generative-adversarial-networks?

Open-source alternatives to mailmahee/pytorch-generative-adversarial-networks include: junyanz/pytorch-cyclegan-and-pix2pix — This project is a deep learning framework designed for training and deploying image-to-image translation models. It… nvidia/pix2pixhd — pix2pixHD is a conditional generative adversarial network designed to transform semantic label maps into… caogang/wgan-gp — A pytorch implementation of Paper "Improved Training of Wasserstein GANs". dmitryulyanov/age — Code for the paper "Adversarial Generator-Encoder Networks". martinarjovsky/wassersteingan — Wasserstein GAN. stormraiser/gan-weight-norm — Code for "On the Effects of Batch and Weight Normalization in Generative Adversarial Networks".

Pytorch Generative Adversarial Networks

Name: mailmahee/pytorch-generative-adversarial-networks
Author: mailmahee

simple generative adversarial network (GAN) using PyTorch

Features

Generative Models - Simple GAN implementation.
Model Implementations - Simple generative adversarial network implementation.
GANs, VAEs, and AEs - Listed in the “GANs, VAEs, and AEs” section of the The Incredible Pytorch awesome list.

How this analysis was created: This summary and feature list were written by an AI model that read the project's README and public documentation pages. Each feature links to the documentation it came from; stars, license and language come straight from the GitHub API. The model does not read the source code, and the analysis is refreshed when the project is re-analysed. Learn more on our About page.

simple generative adversarial network (GAN) using PyTorch

Features

Generative Models - Simple GAN implementation.
Model Implementations - Simple generative adversarial network implementation.
GANs, VAEs, and AEs - Listed in the “GANs, VAEs, and AEs” section of the The Incredible Pytorch awesome list.

DmitryUlyanov/AGE

287View on GitHub

Code for the paper "Adversarial Generator-Encoder Networks"

junyanz/pytorch-CycleGAN-and-pix2pix

24,951View on GitHub

This project is a deep learning framework designed for training and deploying image-to-image translation models. It serves as a research platform for experimenting with neural network architectures that transform visual content between distinct stylistic domains, supporting both paired and unpaired training data. The framework distinguishes itself through its support for cycle-consistency constraints, which allow for image translation between domains without requiring corresponding paired examples. It provides a structured pipeline that utilizes adversarial loss optimization, where generator

caogang/wgan-gp

1,548View on GitHub

A pytorch implementation of Paper "Improved Training of Wasserstein GANs"

martinarjovsky/WassersteinGAN

3,243View on GitHub