Unifying Variational Autoencoder (VAE) implementations in Pytorch (NeurIPS 2022)

This repository contains model-free deep reinforcement learning algorithms implemented in Pytorch

Python package with source code from the course "Creative Applications of Deep Learning w/ TensorFlow"

Official implementation of CVPR2020 paper "Learning to Dress 3D People in Generative Clothing" https://arxiv.org/abs/1907.13615

Stochastic Adversarial Video Prediction

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）

Tensorflow code of "autoencoding beyond pixels using a learned similarity metric"

dcgan combined with vae in pytorch!

本项目实现了一种基于 VAE-CycleGAN 的图像重建无监督缺陷检测算法。该算法结合了变分自编码器 (VAE) 和 CycleGAN 的优势，无需标注数据即可检测图像中的缺陷/异常。This project implements an unsupervised defect detection algorithm for image reconstruction based on VAE-CycleGAN. This algorithm combines the advantages of variational autoencoders (VAE) and CycleGAN to detect defects in images without any supervision.

Implementations of various Deep Learning models in PyTorch and TensorFlow.

Code and notebooks related to the paper: "Reconstructing Faces from fMRI Patterns using Deep Generative Neural Networks" by VanRullen & Reddy, 2019

A VAE-GAN model designed for learning 3d shape from a single 2d image. Trained on ShapeNetCore Dataset

Variational Autoencoder-Generative Adversarial Network (VAE-GAN) to hide data inside images

Simple Tensorflow implementation of the paper Autoencoding Beyond Pixels Using a Similarity Metric

Hybrid Architecture Network, originally VAE+CPPN+GAN by hardmaru

Implementation of https://arxiv.org/pdf/1805.12352.pdf (ICLR 2019)

Repository of all notebooks used in the GANs and VAEs event.

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