#计算机科学#Annotated, understandable, and visually interpretable PyTorch implementations of: VAE, BIRVAE, NSGAN, MMGAN, WGAN, WGANGP, LSGAN, DRAGAN, BEGAN, RaGAN, InfoGAN, fGAN, FisherGAN
#计算机科学#Programming assignments and quizzes from all courses within the GANs specialization offered by deeplearning.ai
Simple Implementation of many GAN models with PyTorch.
Keras implementations of Generative Adversarial Networks. GANs, DCGAN, CGAN, CCGAN, WGAN and LSGAN models with MNIST and CIFAR-10 datasets.
A minimal implementaion (less than 150 lines of code with visualization) of DCGAN/WGAN in PyTorch with jupyter notebooks
GAN and VAE implementations to generate artificial EEG data to improve motor imagery classification. Data based on BCI Competition IV, datasets 2a. Final project for UCLA's EE C247: Neural Networks an...
stock prediction with GAN and WGAN
#计算机科学#Creating Anime Faces using Generative Adversarial Networks (GAN) techniques such as: DCGAN, WGAN, StyleGAN, StyleGAN2 and StyleGAN3. Top repos on GitHub for AnimeFace GAN Generative AI Models
Repository for implementation of generative models with Tensorflow 1.x
#计算机科学#Generative Adversarial Networks with TensorFlow2, Keras and Python (Jupyter Notebooks Implementations)
This Repository Contains Solution to the Assignments of the Generative Adversarial Networks (GANs) Specialization from deeplearning.ai on Coursera Taught by Sharon Zhou, Eda Zhou, Eric Zelikman
Generative Deep Learning Sessions led by Anugraha Sinha (Machine Learning Tokyo)
#计算机科学#Dress styles generation using GANs using TensorFlow
Use metropolis hasting to enhance gan on stock prediction
Vanilla GAN and WGAN implementations in PyTorch on the FashionMNIST dataset
GAN, SSGAN, WGAN, and VAE are neural networks for content generation. GAN generates realistic images, SSGAN improves quality, WGAN ensures stability, and VAE compresses data to learn features. Applica...
PyTorch implementations of Generative Adversarial Network series
#自然语言处理#Implementation of https://arxiv.org/pdf/1805.12352.pdf (ICLR 2019)
