Topic: wasserstein-gan Goto Github
Some thing interesting about wasserstein-gan
Some thing interesting about wasserstein-gan
wasserstein-gan,
User: 1konny
wasserstein-gan,Generating Text through Adversarial Training(GAN) using Skip-Thought Vectors
User: afrozas
wasserstein-gan,A GAN framework
User: andreaferretti
wasserstein-gan,Code for the paper "TadGAN: Time Series Anomaly Detection Using Generative Adversarial Networks"
User: arunppsg
wasserstein-gan,PyTorch implementation of Wasserstein GAN paper
User: cedrickchee
wasserstein-gan,Mode collapse example of GANs in 2D (PyTorch).
User: christophreich1996
wasserstein-gan,Wasserstein GAN implementation with keras
User: daigo0927
wasserstein-gan,Keras implementation of WGAN GP for face generation. The model is trained on CelebA dataset.
Organization: data-science-kosta
wasserstein-gan,Unsupervised Domain Adaptation for Acoustic Scene Classification with Wasserstein Distance
User: dr-costas
Home Page: https://arxiv.org/abs/1904.10678
wasserstein-gan,TensorFlow 2.0 implementation of Improved Training of Wasserstein GANs
User: drewszurko
Home Page: https://arxiv.org/abs/1704.00028
wasserstein-gan,Generating shoes with GANs in sake of lulz and education
User: dvmazur
wasserstein-gan,Pytorch implementation of Wasserstein GANs with Gradient Penalty
User: emiliendupont
wasserstein-gan,Wasserstein BiGAN (Bidirectional GAN trained using Wasserstein distance)
User: fmu2
wasserstein-gan,Torch implementation of Wasserstein GAN https://arxiv.org/abs/1701.07875
User: fonfonx
wasserstein-gan,TensorFlow implementation of CipherGAN
Organization: for-ai
Home Page: https://openreview.net/forum?id=BkeqO7x0-
wasserstein-gan,chainer implementation of VAE-GAN, Wasserstein GAN (WGAN), CycleGAN
User: fukuta0614
wasserstein-gan,A conditional Wasserstein Generative Adversarial Network with gradient penalty (cWGAN-GP) for stochastic generation of galaxy properties in wide-field surveys
User: georgehalal
wasserstein-gan,ATTENTION! This is a mirror of the following GitLab project:
User: gillesdegottex
Home Page: https://gitlab.com/gillesdegottex/percivaltts
wasserstein-gan,Brain T1-Weighted MRI Images Classification and WGAN Generation (Alzheimer's and Healthy patients) for the purpose of data augmentation. Implemented in TensorFlow, trained on ADNI dataset.
User: giocoal
wasserstein-gan,Reimplementation of Wasserstein Auto Encoder (WAE) with Wasserstein GAN based penalty D_Z in Tensorflow
User: hiwonjoon
wasserstein-gan,Generating Atari Images with WGANs in PyTorch
User: horrible22232
wasserstein-gan,Chainer implementation of the Wesserstein GAN
User: hvy
wasserstein-gan,Tensorflow Implementation on "The Cramer Distance as a Solution to Biased Wasserstein Gradients" (https://arxiv.org/pdf/1705.10743.pdf)
User: jiamings
wasserstein-gan,Implementation of Wasserstein GAN using Knet
Organization: knetml
wasserstein-gan,Implementation of Wasserstein Generative Adversarial Networks using Tensorflow
User: kpandey008
wasserstein-gan,Improved Wasserstein GAN (WGAN-GP) application on medical (MRI) images
User: laurahanu
wasserstein-gan,In this work we propose two postprocessing approaches applying convolutional neural networks (CNNs) either in the time domain or the cepstral domain to enhance the coded speech without any modification of the codecs. The time domain approach follows an end-to-end fashion, while the cepstral domain approach uses analysis-synthesis with cepstral domain features. The proposed postprocessors in both domains are evaluated for various narrowband and wideband speech codecs in a wide range of conditions. The proposed postprocessor improves speech quality (PESQ) by up to 0.25 MOS-LQO points for G.711, 0.30 points for G.726, 0.82 points for G.722, and 0.26 points for adaptive multirate wideband codec (AMR-WB). In a subjective CCR listening test, the proposed postprocessor on G.711-coded speech exceeds the speech quality of an ITU-T-standardized postfilter by 0.36 CMOS points, and obtains a clear preference of 1.77 CMOS points compared to G.711, even en par with uncoded speech.
Organization: linksense
Home Page: https://ansleliu.github.io/CNN.html
wasserstein-gan,Keras implementation of Deep Learning Models applied to the MNIST and Polynomial datasets. Repository for the Software and Computing for Nuclear and Subnuclear Physics Project.
User: lorenzovalente3
wasserstein-gan,This repository deals with analyzing various Neural Network approaches and finding the one with the most accurate reconstruction of motion captured trajectories recorded with missing markers in softwares like Vicon Nexus
User: meetgandhi
wasserstein-gan,Vanilla GAN and WGAN implementations in PyTorch on the FashionMNIST dataset
User: mickypaganini
wasserstein-gan,Sampling from the solution of the Zakai equation, using the Signature and Conditional Wasserstein GANs
User: msabvid
wasserstein-gan,Metropolis-Hastings GAN in Tensorflow for enhanced generator sampling
User: nardeas
wasserstein-gan,Data and Trained models can be downloaded from https://goo.gl/7PrKD2
User: nghorbani
wasserstein-gan,My version of cWGAN-gp. Simply my cDCGAN-based but using the Wasserstein Loss and gradient penalty.
User: nicelycla
wasserstein-gan,We've applied the Reptile algorithm to our GAN architectures. The peculiarity is the exclusion of G from meta-learning. Surprisingly, everything worked and the research was published in a paper. More details reported on the paper "Towards Latent Space Optimization of GANs Using Meta-Learning" and the thesis (Italian).
User: nicelycla
wasserstein-gan,Progressive Growing of GANS
User: omidsakhi
wasserstein-gan,Wasserstein GAN with Gradient Penalty in DL4S
User: palle-k
wasserstein-gan,Implementation of some types of GANs (Deep convolutional GAN - Wasserstein GAN - conditional GAN) with PyTorch library
User: parham1998
wasserstein-gan,Pure tensorflow implementation of progressive growing of GANs
User: preritj
wasserstein-gan,Unofficial PyTorch implementation of "Progressive Growing of GANs for Improved Quality, Stability, and Variation".
User: rahulbhalley
Home Page: https://arxiv.org/abs/1710.10196
wasserstein-gan,Source code for "Training Generative Adversarial Networks Via Turing Test".
User: rahulbhalley
Home Page: https://arxiv.org/abs/1810.10948
wasserstein-gan,My implementations of deep neural networks for practice.
User: shaform
wasserstein-gan,PyTorch implementation of WGAN-GP-based video generation. Includes functionality for measuring Frechet Video Distance and implementing recent research improvements of WGAN-GP. Read paper at https://github.com/talcron/frame-prediction-pytorch/blob/media/paper.pdf
User: talcron
wasserstein-gan,DCGAN and WGAN implementation on Keras for Bird Generation
User: tensorfreitas
wasserstein-gan,Keras implementation of "Image Inpainting via Generative Multi-column Convolutional Neural Networks" paper published at NIPS 2018
User: tlatkowski
wasserstein-gan,mxnet implement for Conditional Wasserstein GAN
User: vsooda
wasserstein-gan,TensorFlow implementation of Wasserstein GAN (WGAN) with MNIST dataset.
User: yeonghyeon
wasserstein-gan,Resources and Implementations of Generative Adversarial Nets: GAN, DCGAN, WGAN, CGAN, InfoGAN
User: yfeng95
wasserstein-gan,A Pytorch implementation demo for WGAN-GP in order to generate handwritten digits(MNIST dataset) Pytorch构建WGAN-GP网络实现手写数字生成(MNIST数据集)
User: zhmou
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