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)
A conditional DCGAN, in Tensorflow, for generating hand-written digits from the MNIST dataset.
Labs for 5003 Deep Learning Practice course in summer term 2021 at NYCU.
A small overview of what GANs and their main variants are, with related implementations.
神经网络模型训练研究学习
outGANfit - a cDCGANs-based architecture
cDCGAN model for audio-to-image generation: a cross-modal analysis using deep-learning techniques
General Adversial Networks using Few shot learning
conditionalDCGAN for MNIST with chainer
GAN-based framework to generate depth images of infants from a desired image and pose
Add a description, image, and links to the cdcgan topic page so that developers can more easily learn about it.
To associate your repository with the cdcgan topic, visit your repo's landing page and select "manage topics."