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72 search results found
Tensorflow Generative Model Collections
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3,570
Collection of generative models in Tensorflow
Pytorch Generative Model Collections
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1,985
Collection of generative models in Pytorch version.
Pi Rec
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1,835
🔥 PI-REC: Progressive Image Reconstruction Network With Edge and Color Domain. 🔥 图像翻译,条件GAN,AI绘画
Awesome Gans
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710
Awesome Generative Adversarial Networks with tensorflow
2019 Ccf Bdci Ocr Mczj Ocr Identificationidelement
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706
2019CCF-BDCI大赛 最佳创新探索奖获得者 基于OCR身份证要素提取赛题冠军 天晨破晓团队 赛题源码
All About The Gan
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602
All About the GANs(Generative Adversarial Networks) - Summarized lists for GAN
Mimicry
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556
[CVPR 2020 Workshop] A PyTorch GAN library that reproduces research results for popular GANs.
Super_resolution_with_cnns_and_gans
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261
Image Super-Resolution Using SRCNN, DRRN, SRGAN, CGAN in Pytorch
Pytorch Mnist Celeba Cgan Cdcgan
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221
Pytorch implementation of conditional Generative Adversarial Networks (cGAN) and conditional Deep Convolutional Generative Adversarial Networks (cDCGAN) for MNIST dataset
Gan Keras
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163
里面包含许多GAN算法的Keras源码,可以用于训练自己的模型。
Nucleisegmentation
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152
cGAN-based Multi Organ Nuclei Segmentation
Fashionplus
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150
Fashion++: Minimal Edits for Outfit Improvement
Cvae Gan Zoos Pytorch Beginner
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116
For beginner, this will be the best start for VAEs, GANs, and CVAE-GAN. This contains AE, DAE, VAE, GAN, CGAN, DCGAN, WGAN, WGAN-GP, VAE-GAN, CVAE-GAN. All use PyTorch.
Pytorch.sngan_projection
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109
An unofficial PyTorch implementation of SNGAN (ICLR 2018) and cGANs with projection discriminator (ICLR 2018).
Tensorflow Mnist Cgan Cdcgan
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106
Tensorflow implementation of conditional Generative Adversarial Networks (cGAN) and conditional Deep Convolutional Adversarial Networks (cDCGAN) for MANIST dataset.
Gans Collections Tf2.0_keras Eager_mode
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85
This repository implements all kinds of GAN-models based on tensorflow2.0 keras API including GAN, CGAN, WGAN, WGAN_GP, VAE, CVAE, LSGAN, infoGAN, EBGAN, BEGAN, ACGAN
Auto_painter
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84
Recently, realistic image generation using deep neural networks has become a hot topic in machine learning and computer vision. Such an image can be generated at pixel level by learning from a large collection of images. Learning to generate colorful cartoon images from black-and-white sketches is not only an interesting research problem, but also a useful application in digital entertainment. In this paper, we investigate the sketch-to-image synthesis problem by using conditional generative adv
Dsgan
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81
Implementation of Diversity-Sensitive Conditional Generative Adversarial Networks (ICLR 2019)
Pytorch Conditional Gans
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61
Implementation of Conditional Generative Adversarial Networks in PyTorch
Anime_gan
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51
GAN models with Anime.
Pytorch Pix2pix
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47
Pytorch implementation of pix2pix for various datasets.
Cgan Face Generator
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46
Face generator from sketches using cGAN (pix2pix) model
Pytorch Gans
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36
Various GANs in pytorch environment
Deep Generative Models
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35
Deep generative models implemented with TensorFlow 2.0: eg. Restricted Boltzmann Machine (RBM), Deep Belief Network (DBN), Deep Boltzmann Machine (DBM), Convolutional Variational Auto-Encoder (CVAE), Convolutional Generative Adversarial Network (CGAN)
Acscp_cgan
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33
Code implementation for paper that "ACSCS: Crowd Counting via Adversarial Cross-Scale Consistency Pursuit"; This is method of Crowd counting by conditional generation adversarial networks
Voice Denoising An
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31
A Conditional Generative Adverserial Network (cGAN) was adapted for the task of source de-noising of noisy voice auditory images. The base architecture is adapted from Pix2Pix.
Bw2color
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30
Imagetodem
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29
Generating Elevation Surface from a Single RGB Remotely Sensed Image Using Deep Learning
Task_driven_data_augmentation
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29
semi-supervised and task driven data augmentation code to improve segmentation performance
Gan Keras
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27
tensorflow2.x implementations of Generative Adversarial Networks.
Ct Gan
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26
A GAN based framework for adding and removing medical evidence in 3D volumetric medical scans
Generating_images_part_by_part
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25
Implementation of the paper "Generating images part by part with composite generative adversarial networks"
Pulmonary Nodule Detection Based On Deep Learning
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25
Using U-net, 3D CNN and cGAN to accomplish Pulmonary Nodule Detection. Also it's my undergraduate research program.
Gan_breast_mammography_segmentation
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23
SegNet-cGAN and UNET-cGAN for Breast Mammography Segmentation
Everybody Dance Now
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22
Implementation of paper everybody dance now for Deep learning course project
3d Cgan
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22
3D conditional GAN for medical image segmentation
Gans Keras
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21
GANs Implementations in Keras
Deepvideoportraits
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20
rebuilding a computer vision project about video reenactment
Cwgan Gp
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20
A keras implementation of conditional wgan-gp
Pytorch Basic Gans
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19
Simple Pytorch implementations of most used Generative Adversarial Network (GAN) varieties.
Mxnet Gan
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18
MXNet Implementation of DCGAN, Conditional GAN, pix2pix
Cgan Regression
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18
Code for the master thesis "Probabilistic Regression using Conditional Generative Adversarial Networks"
Mxnet Wgan
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18
mxnet implement for Conditional Wasserstein GAN
Residual Dense Network Trained With Cgan For Super Resolution
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17
This repository is as a research project in the field of super resolution. It uses RDN as the generator and spectral norm is used in discriminator.
Dp Cgan
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16
Differentially Private Conditional Generative Adversarial Network
Conditional Gans Pytorch
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16
CGAN ProjectionCGAN ACGAN InfoGAN Pytorch
Cell Nuclei Segmentation Using Cgan
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14
Gan
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14
Generative Adversarial Network
Rocgan
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14
Chainer implementation of the paper Robust Conditional Generative Adversarial Networks
Triple Gan
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13
Pytorch for Triple-GAN
Pytorch_gan
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13
Spectral Normalization and Projection Discriminator
Pix2pix
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12
Tensorflow implementation of pix2pix(cGAN) for audio source separation
Ganregression
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11
Light Field Depth Estimation
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10
LIght Filed Depth Estimation using GAN
Gan_collections
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10
Nas_cgan
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9
cGAN with NAS (arXiv).
Multi Spectral Image Synthesis For Crop Weed Segmentation In Precision Farming
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8
In this work, we propose an alternative solution with respect to the common data augmentation techniques, applying it to the fundamental problem of crop/weed segmentation in precision farming. Starting from real images, we create semi-artificial samples by replacing the most relevant object classes (i.e., crop and weeds) with synthesized counterparts. To do that, we employ a conditional GAN (cGAN), where the generative model is trained by conditioning the shape of the generated object. Moreover
Multiple Gan Tensorflow Mnist
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8
Implement multiple gan including vanilla_gan, dcgan, cgan, infogan and wgan with tensorflow and dataset including mnist.
Microscopygan
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8
🔬Multi-defect microscopy image restoration under limited data conditions
Adversarial_structure_matching
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8
Adversarial Structure Matching for Structured Prediction Tasks
Mdvsfa_cgan
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8
The tensorflow and pytorch implementations of the MDvsFA_cGAN model which is proposed in ICCV2019 paper "Huan Wang, Luping Zhou and Lei Wang. Miss Detection vs. False Alarm: Adversarial Learing for Small Object Segmentation in Infrared Images. International Conference on Computer Vision, Oct.27-Nov.2,2019. Seoul, Republic of Korea".
Uniqueprintv1
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7
Synthesizing Fingerprint from Pattern Type Analysis Features using cGAN - WITC 2019
Gan Pytorch
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7
Collection of GAN Algorithms
Fast Image Gans
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7
Using Deep Learning to create fake images of games using PyTorch
Cgan Keras
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7
Unimodal Conditional Generative Adversarial Network using Keras and MNIST dataset
Cloudsat Gan
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6
Reconstruction of Cloud Vertical Structure with a Generative Adversarial Network
Speakergan
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6
Unofficial implement with paper SpeakerGAN: Speaker identification with conditional generative adversarial network
Generative_adversarial_networks_gan__overview
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5
A small overview of what GANs and their main variants are, with related implementations.
Gans
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5
Study Generative Adversarial Networks
Overpainter
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5
A line painter base on CGAN
Channel_estimation_cgan
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5
Channel Estimation for One-Bit Multiuser Massive MIMO Using Conditional GAN
Supervised Gan
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5
Code for Supervised GANs
Moco_cgan
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5
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