[WIP] torchbearer.variational

A Variational Auto-Encoder library for PyTorch with torchbearer

Contents

• About
• Installation
• Goals

About

Torchbearer.variational is a companion package to torchbearer which is intended to re-implement state of the art models and practices relating to the world of Variational Auto-Encoders (VAEs). The goal is to provide everything from useful abstractions to complete re-implementations of papers. This is in order to support both research and teaching / learning regarding VAEs.

Installation

TBC

Goals

Currently, variational only includes abstractions for simple VAEs and some accompaniments, the next steps are as follows:

• Construct some separate part of the docs for the variational content
• Implement a series of standard models with associated notes pages and example usages
• Implement other divergences not in PyTorch such as MMD, Jensen-Shannon, etc.
• Implement and document tools for sampling the latent spaces of models and producing figures
• Implement other dataloaders not in torchvision and add associated docs