Spectral Normalization for Keras Dense and Convolution Layers

Build and train Lipschitz constrained networks: TensorFlow implementation of k-Lipschitz layers

An unofficial Pytorch implementation of SNGAN, achieving IS of 8.21 and FID of 14.21 on CIFAR-10.

[NeurIPS 2021] Why Spectral Normalization Stabilizes GANs: Analysis and Improvements

🌈 Spectral Normalization implemented as Tensorflow 2

Reimplementation of the Paper: Large Scale GAN Training for High Fidelity Natural Image Synthesis

A implement of spectral normalization GAN for tensorflow version

Image colorization with generative adversarial networks on the CIFAR10 dataset.

Generating super-resolution images using GANs

Code for the paper "Mean Spectral Normalization"

Blind Deblurring using improvements on different GAN models

Spectral Normalization for Generative Adversarial Networks

GANs: Losses, Regularizations and Normalizations

In this repository, we deal with the task of implementing Generative Adversarial Networks (GANs) using the CIFAR-10 dataset. Two popular GANs: DCGAN and SAGAN are implemented. The performance of the network is evaluated using the FID score.

Implementation of GAN papers on Keras and Tensorflow 2.0

Surrogates for microstructure property linkages that inherently fulfill the Voigt-Reuss bounds.

Unofficial PyTorch Implementation of Spectral Normalization for Generative Adversarial Networks (SNGAN) with specialization in Anime faces generation

Implementation of InfoGAN using PyTorch lightning

Implementations of GANs in PyTorch for Pokemon image generation

No description provided.

A template repository for GANs

A Wasserstein Generative Adversarial Network that learns the distribution of a Mixture of Gaussian, using weight clipping or spectral normalization

Running Monte Carlo - Markov Chain algorithm on synthesized spectral models made by CLOUDY to compare them with data from CECILIA survey

Spectrally Normalized GAN trained on Flowers102 dataset

Incremental implementation of GAN and DCGAN using PyTorch for MNIST dataset

This 'Generative Adversarial Network' project was implemented in grad course CSE-676 : Deep Learning [Fall 2019 @UB_SUNY] Course Instructor : Sargur N. Srihari(https://cedar.buffalo.edu/~srihari/)

Solving a deep learning challenge using different types of GAN

Gradually building generative adversarial networks

An unofficial implementation of sigma reparam [Zhai et al. 2023]

No description provided.