Announcing AutoEncoderToolkit.jl: A New Package for Training Autoencoders
We are excited to introduce AutoEncoderToolkit.jl, a new package designed to simplify the training and usage of Flux.jl-based autoencoders and variational autoencoders (VAEs), with a strong probabilistic perspective.
Key Features:
- Probabilistic Focus: Variational encoders and decoders are defined by the log-probability distribution they encode.
- Multiple VAE Flavors: Includes (so far) β-VAE, MMD-VAE, InfoMax-VAE, HVAE, and RHVAE.
- Modular Design: Easily implement new encoder/decoder architectures and VAE variants thanks to Julia’s multiple dispatch.
- Differential-Geometry Perspective: Early stages of implementing a differential-geometry perspective on VAEs to better explore the learned latent space.
- Simple Installation: Install via Julia’s package manager.
- GPU Support: Train models on CUDA-compatible GPUs effortlessly.
- Extensive Documentation: Detailed documentation and highly annotated code enable quick onboarding for contributors with Julia experience.
Contributions Welcome: We are looking for contributors to expand the list of available models. Check our GitHub repository for more details.
For comprehensive documentation and examples, visit our documentation page.