GitHub

01_Variational_AutoEncoder-checkpoint.ipynb

"train_dataset = MNIST(dataset_path, transform=mnist_transform, train=True, download=True)\n", "test_dataset = MNIST(dataset_path, transform=mnist_transform, train ...
Visual Studio Magazine

Generating Synthetic Data Using a Variational Autoencoder with PyTorch

Generating synthetic data is useful when you have imbalanced training data for a particular class, for example, generating synthetic females in a dataset of employees that has many males but few ...
GitHub

ti2ni1m/Image-Generation-with-Conditional-Variational-Autoencoder-C-VAE-and-Conditional-GAN-C-GAN-

In this project, I aim to create a set of images of Kuzushiji Japanese characters via the Kuzushiji-49 dataset using 2 graphical models which are Conditional-Variational Autoencoder(C-VAE), and ...
Microsoft

Crystal Diffusion Variational Autoencoder for Periodic Material Generation

Generating the periodic structure of stable materials is a long-standing challenge for the material design community. This task is difficult because stable materials only exist in a low-dimensional ...
IEEE

Conditional Generative Flow-Induced Variational Autoencoder for Plasma Instances Augmentation

Abstract: In this article, we propose a novel conditional generative flow-induced variational autoencoder (CGlow-VAE) model to address the critical challenge of the small sample issue in plasma ...
IEEE

Camouflaged Variational Graph AutoEncoder Against Attribute Inference Attacks for Cross-Domain Recommendation

Abstract: Cross-domain recommendation (CDR) aims to alleviate the data sparsity problem by leveraging the benefits of modeling two domains. However, existing research often focuses on the ...