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 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
A short demonstration of GANs learning a probability distribution
Beginner's Guide to building GAN from scratch with Tensorflow and Keras
Repository for my research on generative modelling of cell images
Collection of generative models in Tensorflow
Pytorch implementation of LSGAN for generating MNIST images.
[CVPR 2021: Oral] In this work, we show that high frequency Fourier spectrum decay discrepancies are not inherent characteristics for existing CNN-based generative models.
Pytorch implementation of LSGAN for generating 112x112images.
🚀 Variants of GANs most easily implemented as TensorFlow2. GAN, DCGAN, LSGAN, WGAN, WGAN-GP, DRAGAN, ETC...
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