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AutoEncoderToolkit.jl

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Welcome to the AutoEncoderToolkit.jl GitHub repository. This package provides a simple interface for training and using Flux.jl-based autoencoders and variational autoencoders in Julia.

Installation

You can install AutoEncoderToolkit.jl using the Julia package manager. From the Julia REPL, type ] to enter the Pkg REPL mode and run:

add AutoEncoderToolkit

Design

The idea behind AutoEncoderToolkit.jl is to take advantage of Julia's multiple dispatch to provide a simple and flexible interface for training and using different types of autoencoders. The package is designed to be modular and allow the user to easily define and test custom encoder and decoder architectures. Moreover, when it comes to variational autoencoders, AutoEncoderToolkit.jl takes a probabilistic perspective, where the type of encoders and decoders defines (via multiple dispatch) the corresponding distribution used within the corresponding loss function.

For more information, please refer to the documentation.

Implemented Autoencoders

model module description
Autoencoder AEs Vanilla deterministic autoencoder
Variational Autoencoder VAEs Vanilla variational autoencoder
β-VAE VAEs beta-VAE to weigh the reconstruction vs. KL divergence in ELBO
MMD-VAEs MMDs Maximum-Mean Discrepancy Variational Autoencoders
InfoMax-VAEs InfoMaxVAEs Information Maximization Variational Autoencoders
Hamiltonian VAE HVAEs Hamiltonian Variational Autoencoders
Riemannian Hamiltonian-VAE RHVAEs Riemannian-Hamiltonian Variational Autoencoder

Notes

Some tests are failing only when running on GitHub Actions. Locally, all tests pass. The error in Github Actions shows up when testing the computation of loss function gradients as:

Got exception outside of a @test

BoundsError: attempt to access 16-element Vector{UInt8} at index [0]

PRs to fix this issue are welcome.

Community Guidelines

Contributing to the Software

For those interested in contributing to AutoEncoderToolkit.jl, please refer to the GitHub repository. The project welcomes contributions to

  • Expand the list of available models.
  • Improve the performance of existing models.
  • Add new features to the toolkit.
  • Improve the documentation.

Reporting Issues or Problems

If you encounter any issues or problems with the software, you can report them directly on the GitHub repository's issues page.

Seeking Support

For support and further inquiries, consider checking the documentation and existing issues on the GitHub repository. If you still do not find the answer, you can open a new issue on the GitHub repository's issues page.

License / Authors

Released under the MIT License.

Author & Maintainer: Manuel Razo-Mejia