Comparison of DCGAN, CapsuleGAN and Variational Autoencoder for Image Generation
-
Updated
May 8, 2020 - Jupyter Notebook
Comparison of DCGAN, CapsuleGAN and Variational Autoencoder for Image Generation
Investigate mapping of articulations from the image space to the latent space using neural networks.
This is a simplified implementation of VQ-GANs written in PyTorch. The architecture is borrowed from the paper "Taming Transformers for High-Resolution Image Synthesis".
Simple Tensorflow implementation of the paper Autoencoding Beyond Pixels Using a Similarity Metric
Implementation of https://arxiv.org/pdf/1805.12352.pdf (ICLR 2019)
deep learning
This repo contains the implementation of a VAE and CVAE and applies that on MNIST dataset for modeling and generating different digits.
work in-progress
The semi-supervised GAN, or SGAN, model is an extension of the GAN architecture that involves the simultaneous training of a supervised discriminator, unsupervised discriminator, and a generator model. The result is both a supervised classification model that generalizes well to unseen examples and a generator model that outputs plausible exampl…
The Pytorch implementation of the NIPS 2018 paper
Handwritten Digit Generation with VAE and GAN are applied.
Official implementation of Action-Conditioned Frame Prediction Without Discriminator
This is a "fork" of the Genhack3 repo for the Demeter's vision team solution.
Variational Autoencoder-Generative Adversarial Network (VAE-GAN) to hide data inside images
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
Add a description, image, and links to the vae-gan topic page so that developers can more easily learn about it.
To associate your repository with the vae-gan topic, visit your repo's landing page and select "manage topics."