pytorch-vae and s-vae-pytorch
About pytorch-vae
ethanluoyc/pytorch-vae
A Variational Autoencoder (VAE) implemented in PyTorch
This is a foundational building block for machine learning engineers and researchers working with deep learning models. It takes in complex data, like images or text, and learns a compressed, meaningful representation of that data. This compressed representation can then be used for generating new, similar data, or for tasks like anomaly detection.
About s-vae-pytorch
nicola-decao/s-vae-pytorch
Pytorch implementation of Hyperspherical Variational Auto-Encoders
This library helps machine learning researchers and practitioners implement and experiment with a specific type of variational auto-encoder called Hyperspherical VAEs (S-VAEs) within their PyTorch projects. It takes input data and processes it using specialized distributions (like von Mises-Fisher) to produce more robust and meaningful latent representations. Data scientists, AI researchers, and deep learning engineers working on generative models or complex data embeddings would find this useful.
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