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higan

id-disentanglement

Face Identity Disentanglement via Latent Space Mapping [SIGGRAPH ASIA 2020]

image_harmonization

Official implementation of "Foreground-aware Semantic Representations for Image Harmonization"

impersonator

PyTorch implementation of our ICCV 2019 paper: Liquid Warping GAN: A Unified Framework for Human Motion Imitation, Appearance Transfer and Novel View Synthesis

inout

Diverse Image Outpainting via GAN Inversion

insgen

[NeurIPS 2021] Data-Efficient Instance Generation from Instance Discrimination

intro_dgm

An Introduction to Deep Generative Modeling: Examples

invertible-resnet

Official Code for Invertible Residual Networks

iseebetter

iSeeBetter: Spatio-Temporal Video Super Resolution using Recurrent-Generative Back-Projection Networks | Python3 | PyTorch | GANs | CNNs | ResNets | RNNs

joint-vae

Pytorch implementation of JointVAE, a framework for disentangling continuous and discrete factors of variation :star2:

l2m-gan

ladder-latent-data-distribution-modelling

In this paper, we show that the performance of a learnt generative model is closely related to the model's ability to accurately represent the inferred \textbf{latent data distribution}, i.e. its topology and structural properties. We propose LaDDer to achieve accurate modelling of the latent data distribution in a variational autoencoder framework and to facilitate better representation learning. The central idea of LaDDer is a meta-embedding concept, which uses multiple VAE models to learn an embedding of the embeddings, forming a ladder of encodings. We use a non-parametric mixture as the hyper prior for the innermost VAE and learn all the parameters in a unified variational framework. From extensive experiments, we show that our LaDDer model is able to accurately estimate complex latent distribution and results in improvement in the representation quality.

lafin

LaFIn: Generative Landmark Guided Face Inpainting

lccgan-v2

Pytorch implementation of LCCGAN++.

lffont

lggan

[CVPR 2020] Local Class-Specific and Global Image-Level Generative Adversarial Networks for Semantic-Guided Scene Generation

linearstyletransfer

This is the Pytorch implementation of "Learning Linear Transformations for Fast Image and Video Style Transfer" (CVPR 2019).

logan

The unofficial implementation of LOGAN: Latent Optimisation for Generative Adversarial Networks

lr-livae

Tensorflow implementation of Disentangling Latent Space for VAE by Label Relevant/Irrelevant Dimensions (CVPR 2019)

malnyun_faces

침착한 생성모델 학습기

mimicry

[CVPR 2020 Workshop] A PyTorch GAN library that reproduces research results for popular GANs.

minegan

GAN, transfer learning, few data.

mixnmatch

ml-visuals

Visuals contains figures and templates which you can reuse and customize to improve your scientific writing.

mmd-gan

Improving MMD-GAN training with repulsive loss function

mmsr

Open MMLab Image and Video Super-Resolution Toolbox, , including SRResNet, SRGAN, ESRGAN, EDVR, etc.

modnet

A Trimap-Free Solution for Portrait Matting in Real Time under Changing Scenes

msg-stylegan-tf

MSG StyleGAN in tensorflow

msgan

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

mwgan

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