Projects sharing features with Improved Gan

A list of all named GANs!

A repository for the paper "Improved Adversarial Systems for 3D Object Generation and Reconstruction".

StyleGAN is a TensorFlow-based generative adversarial network framework designed for the synthesis of high-resolution synthetic imagery. It utilizes a style-based generator architecture to create realistic visual assets from latent vectors, focusing on the production of high-fidelity images. The system incorporates style mixing and stochastic noise injection to control visual attributes and fine-grained details. It uses adaptive instance normalization and progressive resolution upsampling to manage image quality and variety across different resolutions. The framework covers the full lifecycl

Pytorch implementation

Code for reproducing key results in the paper "InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets"

pix2pix is a framework for image-to-image translation using conditional generative adversarial networks. It functions as a supervised trainer and visual domain mapper designed to learn a mapping between input and output images for style and domain transfer. The system utilizes a U-Net encoder-decoder architecture combined with a PatchGAN local discriminator to enforce high-frequency local consistency. It employs L1 loss regularization to ensure generated outputs remain structurally close to the ground truth. The project covers a broad range of computer vision capabilities, including semantic

Awesome-GANs is a curated resource list and research repository focused on the development and evaluation of generative adversarial networks. It serves as a structured index for academic literature and open-source implementations dedicated to the creation of synthetic data generators. The project provides a framework for training competing neural networks to produce outputs that mimic the statistical properties of original datasets. It emphasizes the use of configuration-driven pipelines to manage model hyperparameters and dataset paths, facilitating reproducible research workflows and standa

Fast Neural Style for Image Style Transform by Pytorch

VEEGAN: Reducing Mode Collapse in GANs using Implicit Variational Learning

RP-GAN: Stable GAN Training with Random Projections

G-SchNet is a generative neural network that samples molecules by sequentially placing atoms in 3d space. It can be trained on data sets of 3d molecules with variable sizes and compositions. The conditional version, cG-SchNet, explicitly takes chemical and structural properties into account to…

The author's officially unofficial PyTorch BigGAN implementation.

Boundary Equibilibrium Generative Adversarial Networks Implementation in Tensorflow

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

Tensorflow implementation of "BEGAN: Boundary Equilibrium Generative Adversarial Networks"

This is a TensorFlow implementation of the Deep Convolutional Generative Adversarial Network (DCGAN) architecture, providing a framework for training generative models that produce synthetic images from random noise vectors. The project implements the core DCGAN design, using transposed convolutions for upsampling, batch normalization for training stability, and leaky ReLU activations in the discriminator, all executed as static TensorFlow computation graphs. The implementation supports training on custom image datasets by accepting user-supplied image folders without requiring a predefined f

PyTorch implementation of "Learning to Discover Cross-Domain Relations with Generative Adversarial Networks"

pytorch implementation of fast-neural-style

A TensorFlow implementation of the Differentiable Neural Computer.

Convolutional neural network model for video classification trained on the Kinetics dataset.