A comprehensive tutorial on how to implement and train variational autoencoder models based on simple gaussian distribution modeling using PyTorch
- TrainSimpleGaussFCVAE notebook demonstrates how to implement and train very simple a fully-connected variational autoencoder with simple gaussian distribution modeling.
A step by step tutorial on how to build and train VGG using PyTorch can be found in my blog post (URL: https://jianzhongdev.github.io/VisionTechInsights/posts/gentle_introduction_to_variational_autoencoders/)
Leanred MNIST manifold:
Leanred MNIST latent space distribution:
This repo has been implemented and tested on the following dependencies:
- Python 3.10.13
- matplotlib 3.8.2
- numpy 1.26.2
- torch 2.1.1+cu118
- torchvision 0.16.1+cu118
- notebook 7.0.6
This repo has been tested on a laptop computer with the following specs:
- CPU: Intel(R) Core(TM) i7-9750H CPU
- Memory: 32GB
- GPU: NVIDIA GeForce RTX 2060
[1] Diederik P. Kingma and Max Welling. 2013. Auto-encoding variational Bayes. arXiv preprint arXiv:1312.6114 (2013).
If you found this article helpful, please cite it as:
Zhong, Jian (July 2024). A Gentle Introduction to Variational Autoencoders: Concept and PyTorch Implementation Guide. Vision Tech Insights. https://jianzhongdev.github.io/VisionTechInsights/posts/gentle_introduction_to_variational_autoencoders/.
Or
@article{zhong2024GentleIntroVAE,
title = "A Gentle Introduction to Variational Autoencoders: Concept and PyTorch Implementation Guide",
author = "Zhong, Jian",
journal = "jianzhongdev.github.io",
year = "2024",
month = "July",
url = "https://jianzhongdev.github.io/VisionTechInsights/posts/gentle_introduction_to_variational_autoencoders/"
}