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Search results for generative adversarial network regularization

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3 search results found

Stylegan2 Tensorflow 2.0 ⭐ 419

StyleGAN 2 in Tensorflow 2.0

Gan Tutorial ⭐ 190

Simple Implementation of many GAN models with PyTorch.

Implicit Competitive Regularization ⭐ 103

Code for: Implicit Competitive Regularization in GANs

Stabilizing_gans ⭐ 78

Code for the NIPS17 paper "Stabilizing Training of Generative Adversarial Networks through Regularization"

Implementation for <Learning towards Minimum Hyperspherical Energy> in NIPS'18.

Code for Unconditional Audio Generation with GAN and Cycle Regularization

Repo for our CVPR Paper: Watch your Up-Convolution: CNN Based Generative Deep Neural Networks areFailing to Reproduce Spectral Distributions

Speech2video ⭐ 35

Code for ACCV 2020 "Speech2Video Synthesis with 3D Skeleton Regularization and Expressive Body Poses"

Gan Manifold Regularization ⭐ 28

This is the code we used in our paper accepted at ICLR 2018

Code for ICLR2018 paper: Improving GAN Training via Binarized Representation Entropy (BRE) Regularization - Y. Cao · W Ding · Y.C. Lui · R. Huang

Simple Implementation Of Ml Algorithms ⭐ 17

My simplest implementations of common ML algorithms

Gan Manifold Reg ⭐ 16

This is the code we used in our paper Manifold regularization with GANs for semi-supervised learning

Wgan Lp Tensorflow ⭐ 13

Reproduction code for WGAN-LP

Deepexperiments ⭐ 9

TensorFlow/Keras experiments on computer vision and natural language processing

Srgans Spectral Regularization Gans ⭐ 9

Gan Hallucination ⭐ 8

This projects investigates the possible hallucinations or adversarial attacks for solving linear inverse problems. The goal is to understand the possible hallucinations, define metrics to quantify the hallucination, and find regularization techniques to make deep reconstruction nets robust against hallucination.

Gan Techniques Tensorflow ⭐ 7

GANs: Losses, Regularizations and Normalizations

Adversarial_lipschitz_regularization ⭐ 6

Adversarial Lipschitz Regularization

Code for Towards a Better Understanding and Regularization of GAN Training Dynamics

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