hwalsuklee/tensorflow-mnist-VAE
TensorFlow implementation of variational auto-encoder for generating and denoising MNIST handwritten digit images.
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This repository implements a variational auto-encoder (VAE) for MNIST based on the Auto-Encoding Variational Bayes paper. It uses TensorFlow to train a generative model that learns a latent representation of digit images. The implementation supports image reproduction, denoising with salt-and-pepper noise, and visualization of learned latent manifolds. The model is trained with configurable latent space dimensionality (2D to 20D).
Frequently asked
- What is hwalsuklee/tensorflow-mnist-VAE?
- TensorFlow implementation of variational auto-encoder for generating and denoising MNIST handwritten digit images.
- Is tensorflow-mnist-VAE open source?
- Yes — hwalsuklee/tensorflow-mnist-VAE is an open-source project tracked on heatdrop.
- What language is tensorflow-mnist-VAE written in?
- hwalsuklee/tensorflow-mnist-VAE is primarily written in Python.
- How popular is tensorflow-mnist-VAE?
- hwalsuklee/tensorflow-mnist-VAE has 499 stars on GitHub.
- Where can I find tensorflow-mnist-VAE?
- hwalsuklee/tensorflow-mnist-VAE is on GitHub at https://github.com/hwalsuklee/tensorflow-mnist-VAE.