Pythae: Unifying Generative Autoencoders in Python - A Benchmarking Use Case
Datasets and Benchmarks Track
Authors: Clément Chadebec, Louis Vincent, Stephanie Allassonniere

In recent years, deep generative models have attracted increasing interest due to their capacity to model complex distributions. Among those models, variational autoencoders have gained popularity as they have proven both to be computationally efficient and yield impressive results in multiple fields. Following this breakthrough, extensive research has been done in order to improve the original publication, resulting in a variety of different VAE models in response to different tasks. In this paper we present \textbf{Pythae}, a versatile \textit{open-source} Python library providing both a \textit{unified implementation} and a dedicated framework allowing \textit{straightforward}, \emph{reproducible} and \textit{reliable} use of generative autoencoder models. We then propose to use this library to perform a case study benchmark where we present and compare 19 generative autoencoder models representative of some of the main improvements on downstream tasks such as image reconstruction, generation, classification, clustering and interpolation. The open-source library can be found at \url{https://github.com/clementchadebec/benchmark_VAE}.