yukara-ikemiya/minimal-sqvae
A minimal Pytorch Implementation of Stochastically Quantized Variational AutoEncoder (SQ-VAE) by Sony
This project offers a straightforward way to compress images or other data into a smaller, discrete representation while preserving key features, and then reconstruct them. It takes raw image data as input and produces a compressed representation that can be used to generate reconstructed images. Machine learning researchers and practitioners working on efficient data representation or generative models would use this.
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Use this if you need a flexible and minimal implementation of a Stochastically Quantized Variational AutoEncoder (SQ-VAE) for research or experimentation with discrete data representations.
Not ideal if you are looking for a pre-trained model for immediate real-world deployment or a user-friendly application without any coding.
Stars
34
Forks
4
Language
Python
License
MIT
Last pushed
Oct 16, 2023
Commits (30d)
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