Unifying Variational Autoencoder (VAE) implementations in Pytorch (NeurIPS 2022)
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Updated
Apr 9, 2024 - Python
Unifying Variational Autoencoder (VAE) implementations in Pytorch (NeurIPS 2022)
Python package with source code from the course "Creative Applications of Deep Learning w/ TensorFlow"
Stochastic Adversarial Video Prediction
This repository contains model-free deep reinforcement learning algorithms implemented in Pytorch
All NLP you Need Here. 目前包含15个NLP demo的pytorch实现(大量代码借鉴于其他开源项目,原先是自己玩的,后来干脆也开源出来)
Implementations of various Deep Learning models in PyTorch and TensorFlow.
Official implementation of CVPR2020 paper "Learning to Dress 3D People in Generative Clothing" https://arxiv.org/abs/1907.13615
Tensorflow code of "autoencoding beyond pixels using a learned similarity metric"
Code and notebooks related to the paper: "Reconstructing Faces from fMRI Patterns using Deep Generative Neural Networks" by VanRullen & Reddy, 2019
This is a Python/Tensorflow 2.0 implementation of the Adversarial Latent AutoEncoders.
Tensorflow implementation of VAE and GAN for MNIST
A implement of GAN-collection for tensorflow version
cVAE, VQ-VAE, VQ-VAE2, cVAE-cGAN, PixelCNN and Gated PixelCNN in tensorflow 2.x and keras
A tensorflow implementation of VAE-GAN. This is the first approach which viewed the discriminator as a loss function to improve.
Towards Generative Modeling from (variational) Autoencoder to DCGAN
Repository of all notebooks used in the GANs and VAEs event.
A VAE-GAN model designed for learning 3d shape from a single 2d image. Trained on ShapeNetCore Dataset
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