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Python 2.7 | 3.5 | 3.6 | 3.7 PyTorch 1.1.0 Build Status Documentation Status

[WIP] torchbearer.variational

A Variational Auto-Encoder library for PyTorch with torchbearer

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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.

The easiest way to install visual is with pip:

pip install torchbearer-variational

Alternatively, build from source with:

pip install git+https://github.com/pytorchbearer/variational

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

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A Variational Auto-Encoder library for PyTorch with torchbearer.

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