22.12.2021 · A collection of Variational AutoEncoders (VAEs) implemented in pytorch with focus on reproducibility. The aim of this project is to provide a quick and simple working example for many of the cool VAE models out there. All the models are trained on the CelebA dataset for consistency and comparison.
01.04.2020 · Deep Feature Consistent Variational Autoencoder in PyTorch. A PyTorch implementation of Deep Feature Consistent Variational Autoencoder. I implemented DFC-VAE based on the paper by Xianxu Hou, Linlin Shen, Ke Sun, Guoping Qiu. I trained this model with CelebA dataset.
Dec 14, 2017 · vae-celebA. Hereby we present plain VAE and modified VAE model, both of which are trained on celebA dataset to synthesize facial images.
May 14, 2020 · Variational autoencoders try to solve this problem. In traditional autoencoders, inputs are mapped deterministically to a latent vector z = e ( x) z = e ( x). In variational autoencoders, inputs are mapped to a probability distribution over latent vectors, and a latent vector is then sampled from that distribution.
A Collection of Variational Autoencoders (VAE) in PyTorch. ... Modular VAE disentanglement framework for python built with PyTorch ... A VAE for CelebA.
Apr 01, 2020 · A PyTorch implementation of Deep Feature Consistent Variational Autoencoder. I implemented DFC-VAE based on the paper by Xianxu Hou, Linlin Shen, Ke Sun, Guoping Qiu. I trained this model with CelebA dataset. For more details about the dataset, please refer to the website. Installation Clone this repository. python 3.6 is recommended.
VAE-pytorch / VAE_celeba.py / Jump to. Code definitions. CelebaDataset Class __init__ Function __len__ Function __getitem__ Function ToTensor Class __call__ Function Conv_Block Class __init__ Function forward Function VAE Class __init__ Function encode Function reparameterize Function decode Function forward Function loss_function Function ...
Python · CelebFaces Attributes (CelebA) Dataset, my CVAE model ... Here I used pytorch to build a model(a weak one) that could generate faces with specific ...
VAE-pytorch / VAE_celeba.py / Jump to Code definitions CelebaDataset Class __init__ Function __len__ Function __getitem__ Function ToTensor Class __call__ Function Conv_Block Class __init__ Function forward Function VAE Class __init__ Function encode Function reparameterize Function decode Function forward Function loss_function Function train ...
Variational Autoencoder. This is a simple variational autoencoder written in Pytorch and trained using the CelebA dataset. The images are scaled down to 112x128, the VAE has a latent space with 200 dimensions and it was trained for nearly 90 epochs.
Some thing interesting about celeba-dataset Here are 60 public repositories ... celeba-dataset,A Collection of Variational Autoencoders (VAE) in PyTorch.
PyTorch VAE. A collection of Variational AutoEncoders (VAEs) implemented in pytorch with focus on reproducibility. The aim of this project is to provide a quick and simple working example for many of the cool VAE models out there. All the models are trained on the CelebA dataset for consistency and comparison. The architecture of all the models ...
GitHub - ku2482/vae.pytorch: A PyTorch Implementation of Deep Feature Consistent Variational Autoencoder. ... I trained this model with CelebA dataset.
Dec 22, 2021 · A collection of Variational AutoEncoders (VAEs) implemented in pytorch with focus on reproducibility. The aim of this project is to provide a quick and simple working example for many of the cool VAE models out there. All the models are trained on the CelebA dataset for consistency and comparison.
It's likely that you've searched for VAE tutorials but have come away empty-handed. Either the tutorial uses MNIST instead of color images or the concepts ...
Collection of generative models, e.g. GAN, VAE in Pytorch and Tensorflow. Also present here are RBM and Helmholtz Machine. Generated samples will be stored ...