ProGAN with Standard, WGAN, WGAN-GP, LSGAN, BEGAN, DRAGAN, Conditional GAN, InfoGAN, and Auxiliary Classifier GAN training methods
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Updated
Sep 6, 2024 - Python
ProGAN with Standard, WGAN, WGAN-GP, LSGAN, BEGAN, DRAGAN, Conditional GAN, InfoGAN, and Auxiliary Classifier GAN training methods
From scratch, simple and easy-to-understand Pytorch implementation of variants of generative adversarial network (GAN). Implemented variants: Conditional GAN (cGAN), DCGAN, LSGAN. Datasets used MNIST, SVHN, FashionMNIST, CIFAR10, CelebA, LSUN-Bedroom, LSUN-Church.
Improved LSGAN using simple loss constraint
The GAN Book: Train stable Generative Adversarial Networks using TensorFlow2, Keras and Python.
The Generative Adversarial Networks with Python would serve as our primary reference throughout the project. The models would be trained on the MNIST dataset. The official TensorFlow framework and documentation will be used to implement the different architectures on Python. These papers would be used to implement various evaluation met
머신러닝 프레임워크를 활용한 비교사(Unsupervised) 학습 모델 구현 프로젝트
Keras implementations of Generative Adversarial Networks. GANs, DCGAN, CGAN, CCGAN, WGAN and LSGAN models with MNIST and CIFAR-10 datasets.
A short demonstration of GANs learning a probability distribution
The following study presents a model for generating chest X-ray images of normal subjects (without lung disease) and pneumonia patients.
GANs 101 and its Applications: An exploration of DCGAN, WGAN, LSGAN and GAN transfer learning on the CelebA dataset
Beginner's Guide to building GAN from scratch with Tensorflow and Keras
MATLAB implementations of Generative Adversarial Networks -- from GAN to Pixel2Pixel, CycleGAN
Repository for my research on generative modelling of cell images
AVmod is a Audiovisual modulator developed with Deep Fake
Generating images in different contexts using GANs and Variational Autoencoders
Collection of generative models in Tensorflow
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