There are 2 repositories under cdcgan topic.
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 :couple: and emojis :smiley: using pytorch
A small overview of what GANs and their main variants are, with related implementations.
Simple and intuitive implementations of Generative Adversarial Networks with pytorch
神经网络模型训练研究学习
Conditional Deep Convolutional GAN implementation using pytorch on MNIST dataset.
outGANfit - a cDCGANs-based architecture
Step by step Generative Adversarial Networks and Conditional GAN building using Pytorch
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.
General Adversial Networks using Few shot learning
conditionalDCGAN for MNIST with chainer
cDCGAN model for audio-to-image generation: a cross-modal analysis using deep-learning techniques
A Conditional Deep Convolutional Generative Adversarial Network implemented in PyTorch, trained on the Fashion MNIST dataset.
GAN-based framework to generate depth images of infants from a desired image and pose
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