本项目使用 PyTorch 对 CelebA 数据集进行训练,构建一个简单的 Variational Autoencoder (VAE),并生成新的头像图像 - YemuRiven/VAE-on-CelebA
PyTorch implementation of Ladder Variational Autoencoders (LVAE) [1]: where the variational distributions q at each layer are multivariate Normal with diagonal covariance. Significant differences from [1] include: skip connections in the generative path: conditioning on all layers above rather than on...
6.PyTorch Geometric tutorial: Graph Autoencoders & Variational Graph Autoencoder 0播放 5.Pytorch Geometric tutorial: Aggregation Functions in GNNs 1播放 4.Pytorch Geometric tutorial: Convolutional Layers - Spectral methods 1播放 3.Pytorch Geometric tutorial: Graph attention networks (GAT) implementation ...
This is the Pytorch implementation for our SDM 2024 paper: Zhiqiang Guo, Guohui Li, Jianjun Li, Chaoyang Wang, Si Shi. DualVAE: Dual Disentangled Variational AutoEncoder for Recommendation. In SDM 2024. Paper Data The interaction data is shared at data/. Training logs and models The logs and...
Pytorch implementation of a Variational Autoencoder trained on CIFAR-10. The encoder and decoder modules are modelled using a resnet-style U-Net architecture with residual blocks. - pi-tau/vae
NVAE is a deep hierarchical variational autoencoder that enables training SOTA likelihood-based generative models on several image datasets. Requirements NVAE is built in Python 3.7 using PyTorch 1.6.0. Use the following command to install the requirements: pip install -r requirements.txt Set up...
This repository contains an implementation of the Gaussian Mixture Variational Autoencoder (GMVAE) based on the paper "A Note on Deep Variational Models for Unsupervised Clustering" by James Brofos, Rui Shu, and Curtis Langlotz and a modified version of the M2 model proposed by D. P. Kingma ...
In particular, it provides the possibility to perform benchmark experiments and comparisons by training the models with the same autoencoding neural network architecture. The feature make your own autoencoder allows you to train any of these models with your own data and own Encoder and Decoder ...
Conditional Variational Autoencoder(CVAE)1是Variational Autoencoder(VAE)2的扩展,在VAE中没有办法对生成的数据加以限制,所以如果在VAE中想生成特定的数据是办不到的。比如在mnist手写数字中,我们想生成特定的数字2,VAE就无能为力了。 因此,CVAE通过对潜层变量和输入数据施加约束,可以生成在某种约束条件下的数据。
Even though Variational Autoencoders (VAEs) are widely used for semi-supervised learning, the reason why they work remains unclear. In fact, the addition of the unsupervised objective is most often vaguely described as a regularization. The strength of this regularization is controlled by down-...