Resources and Implementations of Generative Adversarial Nets: GAN, DCGAN, WGAN, CGAN, InfoGAN
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Updated
Sep 8, 2017 - Python
Resources and Implementations of Generative Adversarial Nets: GAN, DCGAN, WGAN, CGAN, InfoGAN
Pytorch implementation of Wasserstein GANs with Gradient Penalty
Code for the paper "TadGAN: Time Series Anomaly Detection Using Generative Adversarial Networks"
Tensorflow Implementation on "The Cramer Distance as a Solution to Biased Wasserstein Gradients" (https://arxiv.org/pdf/1705.10743.pdf)
TensorFlow implementation of CipherGAN
Keras implementation of "Image Inpainting via Generative Multi-column Convolutional Neural Networks" paper published at NIPS 2018
DCGAN and WGAN implementation on Keras for Bird Generation
ProGAN with Standard, WGAN, WGAN-GP, LSGAN, BEGAN, DRAGAN, Conditional GAN, InfoGAN, and Auxiliary Classifier GAN training methods
Torch implementation of Wasserstein GAN https://arxiv.org/abs/1701.07875
Generating Text through Adversarial Training(GAN) using Skip-Thought Vectors
Mode collapse example of GANs in 2D (PyTorch).
Wasserstein BiGAN (Bidirectional GAN trained using Wasserstein distance)
Pure tensorflow implementation of progressive growing of GANs
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 d…
TensorFlow 2.0 implementation of Improved Training of Wasserstein GANs
Improved Wasserstein GAN (WGAN-GP) application on medical (MRI) images
My implementations of deep neural networks for practice.
chainer implementation of VAE-GAN, Wasserstein GAN (WGAN), CycleGAN
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