Collection of generative models in Pytorch version.

Pytorch implementation of conditional Generative Adversarial Networks (cGAN) and conditional Deep Convolutional Generative Adversarial Networks (cDCGAN) for MNIST dataset

Tensorflow implementation for Conditional Convolutional Adversarial Networks.

Tensorflow implementation of conditional Generative Adversarial Networks (cGAN) and conditional Deep Convolutional Adversarial Networks (cDCGAN) for MANIST dataset.

My implementation of various GAN (generative adversarial networks) architectures like vanilla GAN (Goodfellow et al.), cGAN (Mirza et al.), DCGAN (Radford et al.), etc.

Pytorch implementation of pix2pix for various datasets.

Text to Image Synthesis using Generative Adversarial Networks

PyTorch implementation of Conditional Deep Convolutional Generative Adversarial Networks (cDCGAN)

[MICCAI'21] [Tensorflow] Retinal Vessel Segmentation using a Novel Multi-scale Generative Adversarial Network

Code implementation for paper that "ACSCS: Crowd Counting via Adversarial Cross-Scale Consistency Pursuit"; This is method of Crowd counting by conditional generation adversarial networks

Conditional Sequence Generative Adversarial Network trained with policy gradient, Implementation in Tensorflow

GAN models with Anime.

Generative Adversarial Networks in TensorFlow 2.0

An enhanced zi2zi project with word-oriented data augmentation, feature combination, and transfer learning.

[NeurIPS 2022, T-PAMI 2023] Efficient Spatially Sparse Inference for Conditional GANs and Diffusion Models

Code for the paper: Multi-Label Clinical Time-Series Generation via Conditional GAN (IEEE TKDE)

Deep learning works for ADLxMLDS (CSIE 5431) in NTU

Official PyTorch implementation of Harmonizing Maximum Likelihood with GANs for Multimodal Conditional Generation (ICLR 2019)

🎨 Anime generation with GANs.

The implementation of 'Image synthesis via semantic composition', ICCV2021.