GitPlanet

Cheap and reliable Node.js hosting starts at $3/month, and $1/month static HTML hosting

Created with love in Canada, visit hostnodejs.com today

Feel like to post an Ad? Learn Details
All Projects → LynnHo → Dcgan Lsgan Wgan Gp Dragan Tensorflow 2

LynnHo / Dcgan Lsgan Wgan Gp Dragan Tensorflow 2

Licence: mit
DCGAN LSGAN WGAN-GP DRAGAN Tensorflow 2

Programming Languages

python
139335 projects - #7 most used programming language

Labels

tensorflowdcganwganwgan-gp

Projects that are alternatives of or similar to Dcgan Lsgan Wgan Gp Dragan Tensorflow 2

Gan Tutorial
Simple Implementation of many GAN models with PyTorch.
Stars: ✭ 227 (-39.14%)
Mutual labels:  dcgan, wgan, wgan-gp
Dcgan wgan wgan Gp lsgan sngan rsgan began acgan pggan tensorflow
Implementation of some different variants of GANs by tensorflow, Train the GAN in Google Cloud Colab, DCGAN, WGAN, WGAN-GP, LSGAN, SNGAN, RSGAN, RaSGAN, BEGAN, ACGAN, PGGAN, pix2pix, BigGAN
Stars: ✭ 166 (-55.5%)
Mutual labels:  dcgan, wgan, wgan-gp
Ganotebooks
wgan, wgan2(improved, gp), infogan, and dcgan implementation in lasagne, keras, pytorch
Stars: ✭ 1,446 (+287.67%)
Mutual labels:  dcgan, wgan, wgan-gp
Tf.gans Comparison
Implementations of (theoretical) generative adversarial networks and comparison without cherry-picking
Stars: ✭ 477 (+27.88%)
Mutual labels:  dcgan, wgan, wgan-gp
Generative-Model
Repository for implementation of generative models with Tensorflow 1.x
Stars: ✭ 66 (-82.31%)
Mutual labels:  dcgan, wgan, wgan-gp
Deeplearningmugenknock
でぃーぷらーにんぐを無限にやってディープラーニングでDeepLearningするための実装CheatSheet
Stars: ✭ 684 (+83.38%)
Mutual labels:  dcgan, wgan, wgan-gp
Awesome Gans
Awesome Generative Adversarial Networks with tensorflow
Stars: ✭ 585 (+56.84%)
Mutual labels:  dcgan, wgan, wgan-gp
Pytorch-Basic-GANs
Simple Pytorch implementations of most used Generative Adversarial Network (GAN) varieties.
Stars: ✭ 101 (-72.92%)
Mutual labels:  dcgan, wgan, wgan-gp
Dcgan Lsgan Wgan Gp Dragan Pytorch
DCGAN LSGAN WGAN-GP DRAGAN PyTorch
Stars: ✭ 134 (-64.08%)
Mutual labels:  dcgan, wgan-gp
Generative adversarial networks 101
Keras implementations of Generative Adversarial Networks. GANs, DCGAN, CGAN, CCGAN, WGAN and LSGAN models with MNIST and CIFAR-10 datasets.
Stars: ✭ 138 (-63%)
Mutual labels:  dcgan, wgan
Gan theories
Resources and Implementations of Generative Adversarial Nets which are focusing on how to stabilize training process and generate high quality images: DCGAN, WGAN, EBGAN, BEGAN, etc.
Stars: ✭ 158 (-57.64%)
Mutual labels:  dcgan, wgan
Pytorch Gan Collections
PyTorch implementation of DCGAN, WGAN-GP and SNGAN.
Stars: ✭ 128 (-65.68%)
Mutual labels:  dcgan, wgan-gp
Tf Exercise Gan
Tensorflow implementation of different GANs and their comparisions
Stars: ✭ 110 (-70.51%)
Mutual labels:  dcgan, wgan
GANs-Keras
GANs Implementations in Keras
Stars: ✭ 24 (-93.57%)
Mutual labels:  dcgan, wgan
Wasserstein Gan
Chainer implementation of Wasserstein GAN
Stars: ✭ 95 (-74.53%)
Mutual labels:  dcgan, wgan
generative deep learning
Generative Deep Learning Sessions led by Anugraha Sinha (Machine Learning Tokyo)
Stars: ✭ 24 (-93.57%)
Mutual labels:  wgan, wgan-gp
Fun-with-MNIST
Playing with MNIST. Machine Learning. Generative Models.
Stars: ✭ 23 (-93.83%)
Mutual labels:  wgan, wgan-gp
progressive growing of GANs
Pure tensorflow implementation of progressive growing of GANs
Stars: ✭ 31 (-91.69%)
Mutual labels:  wgan, wgan-gp
coursera-gan-specialization
Programming assignments and quizzes from all courses within the GANs specialization offered by deeplearning.ai
Stars: ✭ 277 (-25.74%)
Mutual labels:  dcgan, wgan
Anime gan
GAN models with Anime.
Stars: ✭ 56 (-84.99%)
Mutual labels:  dcgan, wgan
View All Similar Projects ➔

Recommendation

  • Our GAN based work for facial attribute editing - AttGAN.

News

  • 8 April 2019: We re-implement these GANs by Tensorflow 2! The old version is here: v1 or in the "v1" directory.
  • PyTorch Version

GANs - Tensorflow 2

Tensorflow 2 implementations of DCGAN, LSGAN, WGAN-GP and DRAGAN.

Exemplar results

Fashion-MNIST

DCGAN LSGAN WGAN-GP DRAGAN

CelebA

DCGAN LSGAN
WGAN-GP DRAGAN

Anime

WGAN-GP DRAGAN

Usage

  • Environment

    • Python 3.6

    • TensorFlow 2.2, TensorFlow Addons 0.10.0

    • OpenCV, scikit-image, tqdm, oyaml

    • we recommend Anaconda or Miniconda, then you can create the TensorFlow 2.2 environment with commands below

      conda create -n tensorflow-2.2 python=3.6

source activate tensorflow-2.2

conda install scikit-image tqdm tensorflow-gpu=2.2

conda install -c conda-forge oyaml

pip install tensorflow-addons==0.10.0

    • NOTICE: if you create a new conda environment, remember to activate it before any other command

      source activate tensorflow-2.2

  • Datasets

  • Examples of training

    • Fashion-MNIST DCGAN

      CUDA_VISIBLE_DEVICES=0 python train.py --dataset=fashion_mnist --epoch=25 --adversarial_loss_mode=gan

    • CelebA DRAGAN

      CUDA_VISIBLE_DEVICES=0 python train.py --dataset=celeba --epoch=25 --adversarial_loss_mode=gan --gradient_penalty_mode=dragan

    • Anime WGAN-GP

      CUDA_VISIBLE_DEVICES=0 python train.py --dataset=anime --epoch=200 --adversarial_loss_mode=wgan --gradient_penalty_mode=wgan-gp --n_d=5

    • see more training exampls in commands.sh

    • tensorboard for loss visualization

      tensorboard --logdir ./output/fashion_mnist_gan/summaries --port 6006
Note that the project description data, including the texts, logos, images, and/or trademarks, for each open source project belongs to its rightful owner. If you wish to add or remove any projects, please contact us at [email protected].