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yqgans's Projects

msgan icon msgan

MSGAN: Mode Seeking Generative Adversarial Networks for Diverse Image Synthesis (CVPR2019)

mwgan icon mwgan

PyTorch implementation of "Multi-marginal Wasserstein GAN" (NeurIPS2019)

ncsn icon ncsn

Noise Conditional Score Networks

omgd icon omgd

Online Multi-Granularity Distillation for GAN Compression (ICCV2021)

pastegan icon pastegan

An pytorch implementation of our NeurIPS paper of PasteGAN: A Semi-Parametric Method to Generate Image from Scene Graph

pono icon pono

Positional Normalization (PONO) and Moment Shortcut (MS)

presgans icon presgans

Prescribed Generative Adversarial Networks

pytorch-glow icon pytorch-glow

PyTorch implementation of "Glow: Generative Flow with Invertible 1x1 Convolutions"

pytorch-stgan icon pytorch-stgan

An unofficial Pytorch implementation of (CVPR2019) STGAN: A Unified Selective Transfer Network for Arbitrary Image Attribute Editing (https://arxiv.org/abs/1904.09709)

pytorch-studiogan icon pytorch-studiogan

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

pytorch_ema icon pytorch_ema

Tiny PyTorch library for maintaining a moving average of a collection of parameters.

relativisticgan icon relativisticgan

Code for replication of the paper "The relativistic discriminator: a key element missing from standard GAN"

relgan-pytorch icon relgan-pytorch

RelGAN: Multi-Domain Image-to-Image Translation via Relative Attributes

research-ganwriting icon research-ganwriting

Source code for ECCV20 "GANwriting: Content-Conditioned Generation of Styled Handwritten Word Images"

restyle-encoder icon restyle-encoder

Official Implementation for "ReStyle: A Residual-Based StyleGAN Encoder via Iterative Refinement" https://arxiv.org/abs/2104.02699

retrieveinstyle icon retrieveinstyle

Official PyTorch implementation of Retrieve in Style: Unsupervised Facial Feature Transfer and Retrieval.

revgan icon revgan

RevGAN implementation in PyTorch. We extend the Pix2pix and CycleGAN framework by exploring approximately invertible architectures in 2D and 3D. These architectures are approximately invertible by design and thus partially satisfy cycle-consistency before training even begins. Furthermore, since invertible architectures have constant memory complexity in depth, these models can be built arbitrarily deep without requiring additional memory. In the paper we demonstrate superior quantitative output on the Cityscapes and Maps datasets at near constant memory budget.

s-vae-pytorch icon s-vae-pytorch

Pytorch implementation of Hyperspherical Variational Auto-Encoders

savi2i icon savi2i

Continuous and Diverse Image-to-Image Translation via Signed Attribute Vectors

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