Collection of generative models in Tensorflow
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Updated
Aug 8, 2022 - Python
Collection of generative models in Tensorflow
Collection of generative models in Pytorch version.
🚀 Variants of GANs most easily implemented as TensorFlow2. GAN, DCGAN, LSGAN, WGAN, WGAN-GP, DRAGAN, ETC...
Least Squares Generative Adversarial Network implemented in Chainer
TensorFlow implementations of Wasserstein GAN with Gradient Penalty (WGAN-GP), Least Squares GAN (LSGAN), GANs with the hinge loss.
ProGAN with Standard, WGAN, WGAN-GP, LSGAN, BEGAN, DRAGAN, Conditional GAN, InfoGAN, and Auxiliary Classifier GAN training methods
[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.
Beginner's Guide to building GAN from scratch with Tensorflow and Keras
Playing with MNIST. Machine Learning. Generative Models.
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.
Repository for my research on generative modelling of cell images
PyTorch implementation of the Least Squares Generative Adversarial Networks
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