#
cyclegan
Here are 140 public repositories matching this topic...
Software that can generate photos from paintings, turn horses into zebras, perform style transfer, and more.
computer-vision
deep-learning
computer-graphics
torch
generative-adversarial-network
gan
image-manipulation
image-generation
gans
pix2pix
cyclegan
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Updated
Mar 4, 2020 - Lua
An implementation of CycleGan using TensorFlow
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Updated
Mar 4, 2020 - Python
Tensorflow implementation for learning an image-to-image translation without input-output pairs. https://arxiv.org/pdf/1703.10593.pdf
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Mar 4, 2020 - Python
[NeurIPS 2018] Visual Object Networks: Image Generation with Disentangled 3D Representation.
pytorch
generative-adversarial-network
gans
pix2pix
3d
cyclegan
disentangled-representations
shape-generation
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Updated
Feb 27, 2020 - Python
Python package with source code from the course "Creative Applications of Deep Learning w/ TensorFlow"
tutorial
course
deep-learning
neural-network
mooc
tensorflow
word2vec
gan
dcgan
pixelcnn
vae
glove
wavenet
magenta
autoregressive
celeba
conditional
vae-gan
cyclegan
nsynth
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Updated
Jan 24, 2020 - Python
A clean and readable Pytorch implementation of CycleGAN
computer-vision
deep-learning
computer-graphics
image-processing
pytorch
artificial-intelligence
generative-adversarial-network
image-generation
cyclegan
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Updated
Mar 3, 2020 - Python
Tensorflow implementation of CycleGAN
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Updated
Mar 4, 2020 - Python
Voice Converter Using CycleGAN and Non-Parallel Data
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Feb 25, 2020 - Python
Generative Models Tutorial with Demo: Bayesian Classifier Sampling, Variational Auto Encoder (VAE), Generative Adversial Networks (GANs), Popular GANs Architectures, Auto-Regressive Models, Important Generative Model Papers, Courses, etc..
tutorial
generative-adversarial-network
dcgan
generative-model
gaussian-mixture-models
auto-regressive-model
gans
bayesian-classifiers
variational-inference
tutorial-code
variational-autoencoder
cyclegan
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Updated
Feb 14, 2020 - Jupyter Notebook
DEPRECATED: Depth Map Estimation from Monocular Images
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Updated
Feb 25, 2020 - Python
Master Deep Learning Algorithms with Extensive Math by Implementing them using TensorFlow
tensorflow
word-embeddings
gru
autoencoder
gans
doc2vec
skip-thoughts
adagrad
cyclegan
deep-learning-mathematics
capsule-network
few-shot-learning
quick-thought
deep-learning-scratch
nadam
deep-learning-math
lstm-math
cnn-math
rnn-derivation
contractive-autonencoders
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Updated
Mar 4, 2020 - Jupyter Notebook
AttentionGAN for Unpaired Image-to-Image Translation & Multi-Domain Image-to-Image Translation
computer-vision
deep-learning
geometry
pytorch
image-generation
multi-domain
gans
adversarial-networks
image-translation
attention-model
cyclegan
image-to-image-translation
ijcnn
unpaired
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Updated
Mar 4, 2020 - Python
Deep Learning Summer School + Tensorflow + OpenCV cascade training + YOLO + COCO + CycleGAN + AWS EC2 Setup + AWS IoT Project + AWS SageMaker + AWS API Gateway + Raspberry Pi3 Ubuntu Core
raspberry-pi
opencv
iot
computer-vision
tensorflow
keras
coco
aws-ec2
ec2-instance
aws-iot
opencv-library
generative-adversarial-networks
raspberry-pi-3
opencv3
cyclegan
haarcascade
sagemaker
yolo-model
ubuntucore
aws-sagemaker
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Feb 13, 2020 - Jupyter Notebook
Symbolic Music Genre Transfer with CycleGAN
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Updated
Mar 3, 2020 - Python
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Updated
Mar 2, 2020 - Python
GANnotation (PyTorch): Landmark-guided face to face synthesis using GANs (And a triple consistency loss!)
pytorch
generative-adversarial-network
gan
cyclegan
face2face
face-synthesis
triple-consistency-loss
landmark-guided
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Updated
Mar 2, 2020 - Python
Code repository for Frontiers article 'Generative Adversarial Networks for Image-to-Image Translation on Multi-Contrast MR Images - A Comparison of CycleGAN and UNIT'
deep-learning
keras
image-processing
neural-networks
unit
generative-adversarial-networks
cyclegan
image-to-image-translation
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Updated
Feb 27, 2020 - Python
TensorFlow implementation of CipherGAN
nlp
deep-learning
tensorflow
generative-adversarial-network
gan
dcgan
pix2pix
wasserstein-gan
cyclegan
ciphergan
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Updated
Dec 18, 2019 - Python
Keras implementation of CycleGAN using a tensorflow backend.
python
machine-learning
deep-neural-networks
deep-learning
keras
image-processing
cyclegan
image-to-image-translation
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Updated
Feb 29, 2020 - Python
Pytorch implementation of BicycleGAN with implementation details
computer-vision
deep-learning
pytorch
generative-adversarial-network
gan
image-translation
cyclegan
bicyclegan
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Updated
Mar 4, 2020 - Python
Zardinality
commented
Sep 30, 2017
There are sunflower2daisy and leopard2tiger results shown in readme, but I don't find any clue about where to download them in code. In https://github.com/luoxier/CycleGAN_Tensorlayer/blob/master/main.py#L32 an optional value for dataset_dir is sunflower2daisy, where can I get it? The author of original paper doesn't seem to provide it.
Pytorch implementation of multitask CycleGAN with auxiliary classification loss
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Updated
Jan 7, 2020 - Python
Official implementation of "Artist Style Transfer Via Quadratic Potential"
pytorch
generative-adversarial-network
gan
style-transfer
neural-networks
generative-art
resnet
convolutional-neural-networks
residual-networks
cyclegan
neural-style-transfer
cyclegan-pytorch
quadratic-potential
cyclegan-qp
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Updated
Feb 28, 2020 - Python
Models generated by CycleGAN
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Updated
Nov 22, 2019
keras implementation of cycle-gan
computer-vision
deep-learning
computer-graphics
keras
gan
image-manipulation
image-generation
cyclegan
cyclegan-keras
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Updated
Jan 8, 2020 - Jupyter Notebook
Comparing different similarity functions for reconstruction of image on CycleGAN. (https://tandon-a.github.io/CycleGAN_ssim/) Training cycleGAN with different loss functions to improve visual quality of produced images
computer-vision
deep-learning
tensorflow
computer-graphics
generative-adversarial-network
gan
image-manipulation
image-generation
perceptual-losses
image-translation
cyclegan
image-to-image-translation
perceptual-similarity
ssim-loss
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Updated
Mar 2, 2020 - Python
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Hi, thanks for your great work.
I'm trying to apply cyclegan on my own custom anime dataset to learn facial expression transfer.
My dataset includes 2000 sad and 2000 happy images.
I run the training process for about 100k iterations.
The problem is that I only see some small changes specially in mouth part. Do you have any suggestion? How can I make network more flexible for changing di