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

Conditional Deep Convolutional Generative Adversarial Network (CDCGAN) that creates artworks

CDCGAN and RL models trained for generating and playing (respectively) Super Mario Bros 2 levels

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

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

Simple and intuitive implementations of Generative Adversarial Networks with pytorch

conditionalDCGAN for MNIST with chainer

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

神经网络模型训练研究学习

General Adversial Networks using Few shot learning

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

Conditional Deep Convolutional GAN implementation using pytorch on MNIST dataset.

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.

Deep learning classifier and image generator for building architecture.

Study Generative Adversarial Networks

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

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

Conditional Deep Convolutional GAN