GAN
Generative Adverserial Networks are an approach to generative data modelling using Deep learning methods.
I have currently implemented :
- DCGAN on CIFAR dataset.
Generative Adverserial Networks are an approach to generative data modelling using Deep learning methods.
I have currently implemented :
StudioGAN is a Pytorch library providing implementations of representative Generative Adversarial Networks (GANs) for conditional/unconditional image generation.
Anycost GAN video | paper | website Anycost GANs for Interactive Image Synthesis and Editing Ji Lin, Richard Zhang, Frieder Ganz, Song Han, Jun-Yan Zh
Training GANs with Stronger Augmentations via Contrastive Discriminator (ICLR 2021) This repository contains the code for reproducing the paper: Train
Gender Bangs Body Side Pose (Yaw) Lighting Smile Face Shape Lipstick Color Painting Style Pose (Yaw) Pose (Pitch) Zoom & Rotate Flush & Eye Color Mout
This is the codebase for Diffusion Models Beat GANS on Image Synthesis.
InterFaceGAN - Interpreting the Latent Space of GANs for Semantic Face Editing Figure: High-quality facial attributes editing results with InterFaceGA
pix2pixHD Project | Youtube | Paper Pytorch implementation of our method for high-resolution (e.g. 2048x1024) photorealistic image-to-image translatio
Progressive Growing of GANs inference in PyTorch with CelebA training snapshot Description This is an inference sample written in PyTorch of the origi
Codebase for Diffusion Models Beat GANS on Image Synthesis.
Official PyTorch repo for GAN's N' Roses. Diverse im2im and vid2vid selfie to anime translation.
Hijack-GAN: Unintended-Use of Pretrained, Black-Box GANs In this work, we propose a framework HijackGAN, which enables non-linear latent space travers
ViTGAN: Training GANs with Vision Transformers A PyTorch implementation of ViTGAN based on paper ViTGAN: Training GANs with Vision Transformers. Refer
PyTorch implementation of Progressive Growing of GANs for Improved Quality, Stability, and Variation. Warning: the master branch might collapse. To ob
PyTorch implementation of VAGAN: Visual Feature Attribution Using Wasserstein GANs This code aims to reproduce results obtained in the paper "Visual F
MTV-TSA: Adaptable GAN Encoders for Image Reconstruction via Multi-type Latent Vectors with Two-scale Attentions. This is the official code release fo
GANs for Biological Image Synthesis This codes implements the ICCV-2017 paper "GANs for Biological Image Synthesis". The paper and its supplementary m
WGAN-GP An pytorch implementation of Paper "Improved Training of Wasserstein GANs". Prerequisites Python, NumPy, SciPy, Matplotlib A recent NVIDIA GPU
OCT-GAN: Neural ODE-based Conditional Tabular GANs (OCT-GAN) Code for reproducing the experiments in the paper: Jayoung Kim*, Jinsung Jeon*, Jaehoon L
DiffGAN-TTS - PyTorch Implementation PyTorch implementation of DiffGAN-TTS: High