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

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

Conditional Deep Convolutional GAN

A conditional DCGAN, in Tensorflow, for generating hand-written digits from the MNIST dataset.

Deep learning classifier and image generator for building architecture.

Labs for 5003 Deep Learning Practice course in summer term 2021 at NYCU.

We use Conditional-DCGAN to generate animated faces 👫 and emojis 😃 using pytorch

Conditional Deep Convolutional GAN implementation using pytorch on MNIST dataset.

A small overview of what GANs and their main variants are, with related implementations.

Study Generative Adversarial Networks

神经网络模型训练研究学习

Simple and intuitive implementations of Generative Adversarial Networks with pytorch

outGANfit - a cDCGANs-based architecture

This Repository contain an IPython notebook of an example implementation of conditional Deep Convolutional Generative Adversarial Networks or cDCGAN or DC cGAN using Tensorflow.Keras Funtional API.

Step by step Generative Adversarial Networks and Conditional GAN building using Pytorch

General Adversial Networks using Few shot learning

A Conditional Deep Convolutional Generative Adversarial Network implemented in PyTorch, trained on the Fashion MNIST dataset.

conditionalDCGAN for MNIST with chainer

GAN-based framework to generate depth images of infants from a desired image and pose

cDCGAN model for audio-to-image generation: a cross-modal analysis using deep-learning techniques