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Jun 30, 2021 - Jupyter Notebook
#
generative-model
Here are 538 public repositories matching this topic...
This repository contains the source code for the paper First Order Motion Model for Image Animation
A tensorflow implementation of "Deep Convolutional Generative Adversarial Networks"
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Jan 6, 2021 - JavaScript
Collection of generative models, e.g. GAN, VAE in Pytorch and Tensorflow.
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Jan 31, 2019 - Python
Code for the paper "Jukebox: A Generative Model for Music"
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Aug 14, 2021 - Python
Collection of generative models in Tensorflow
tensorflow
gan
mnist
infogan
generative-model
vae
ebgan
generative-adversarial-networks
wgan
cvae
lsgan
variational-autoencoder
began
cgan
wgan-gp
generative-models
dragan
acgan
fashion-mnist
improved-wgan
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Jul 21, 2018 - Python
[CVPR2020] Adversarial Latent Autoencoders
python
machine-learning
computer-vision
deep-learning
neural-network
paper
pytorch
generative-adversarial-network
gan
generative-model
autoencoder
celeba
paper-implementations
face-generation
pytorch-implementation
celeba-hq
stylegan
ffhq
cvpr2020
alae
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Jan 23, 2021 - Python
Simplest working implementation of Stylegan2, state of the art generative adversarial network, in Pytorch. Enabling everyone to experience disentanglement
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Aug 6, 2021 - Python
Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization
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Oct 30, 2017 - Lua
PyTorch implementation of "Learning to Discover Cross-Domain Relations with Generative Adversarial Networks"
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Mar 26, 2018 - Jupyter Notebook
Tensorflow implementation of "BEGAN: Boundary Equilibrium Generative Adversarial Networks"
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Mar 26, 2018 - Python
Generative Adversarial Networks (GANs) resources sorted by citations
generative-model
gans
adversarial-networks
generative-adversarial-networks
research-paper
frameworks
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May 11, 2019
machine-learning
natural-language-processing
computer-vision
deep-learning
generative-model
semi-supervised-learning
graph-neural-networks
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Jul 26, 2021
Speech Enhancement Generative Adversarial Network in TensorFlow
deep-neural-networks
deep-learning
tensorflow
speech
gan
generative-model
generative-adversarial-networks
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Jan 14, 2021 - Python
This repository contains the code for the CVPR 2021 paper "GIRAFFE: Representing Scenes as Compositional Generative Neural Feature Fields"
generative-adversarial-network
generative-model
nerf
implicit-surfaces
generative-modelling
cvpr2021
neural-scene-representations
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Aug 1, 2021 - Python
TensorFlow implementation of "Learning from Simulated and Unsupervised Images through Adversarial Training"
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Dec 10, 2019 - Python
A simplified PyTorch implementation of "SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient." (Yu, Lantao, et al.)
nlp
natural-language-processing
deep-learning
generative-adversarial-network
gan
generative-model
policy-gradient
natural-language-understanding
seqgan
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Sep 27, 2018 - Python
A curated list of awesome work on VAEs, disentanglement, representation learning, and generative models.
deep-learning
generative-model
vae
awesome-list
representation-learning
unsupervised-learning
variational-inference
variational-autoencoder
variational-bayes
disentanglement
variational-auto-encoder
disentangled-representations
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Jul 11, 2021
PyTorch Re-Implementation of "Generating Sentences from a Continuous Space" by Bowman et al 2015 https://arxiv.org/abs/1511.06349
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Oct 21, 2020 - Python
DanceNet -💃 💃 Dance generator using Autoencoder, LSTM and Mixture Density Network. (Keras)
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Sep 15, 2019 - Python
The idea of this list is to collect shared data and algorithms around 3D Morphable Models. You are invited to contribute to this list by adding a pull request. The original list arised from the Dagstuhl seminar on 3D Morphable Models https://www.dagstuhl.de/19102 in March 2019.
machine-learning
computer-vision
shape
image-processing
generative-model
face-recognition
face-detection
faces
3d
3d-reconstruction
3d-graphics
3d-models
morphable-model
analysis-by-synthesis
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Jul 29, 2021
alexjc
commented
Jul 11, 2020
The quality of images is higher when the number of pixels from the source and target match 1:1. When a larger source is provided, it could be randomly cropped if a command-line argument is set (likely on
by default).
Efficient Graph Generation with Graph Recurrent Attention Networks, Deep Generative Model of Graphs, Graph Neural Networks, NeurIPS 2019
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Oct 20, 2020 - C++
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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Jan 21, 2019 - Jupyter Notebook
Tensorflow Implementation of Knowledge-Guided CVAE for dialog generation ACL 2017. It is released by Tiancheng Zhao (Tony) from Dialog Research Center, LTI, CMU
deep-learning
end-to-end
chatbot
generative-model
dialogue-systems
cvae
variational-autoencoder
variational-bayes
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Nov 26, 2018 - OpenEdge ABL
High-fidelity performance metrics for generative models in PyTorch
reproducible-research
metrics
evaluation
pytorch
gan
generative-model
reproducibility
precision
inception-score
frechet-inception-distance
kernel-inception-distance
perceptual-path-length
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Jul 6, 2021 - Python
DGMs for NLP. A roadmap.
natural-language-processing
parsing
text-generation
generative-text
generative-model
graphical-models
mcmc
generative-adversarial-networks
variational-inference
markov-chain-monte-carlo
latent-variable-models
normalizing-flows
structured-prediction
discrete-structures
generative-models
variational-autoencoders
approximate-inference
gradient-estimation
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May 11, 2021
Stacked Generative Adversarial Networks
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May 14, 2017 - Python
Tensorflow Implementation of the paper [Neural Discrete Representation Learning](https://arxiv.org/abs/1711.00937) (VQ-VAE).
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Apr 5, 2018 - Jupyter Notebook
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This needs to be done in 2 parts:
We don't need to expose all the functions. Some functions do nothing fancy and they need to be removed and the entire computation can be performed inside the
forward
function.