Variational-Auto-Encoder
PyTorch implementation of Variational Auto-Encoder as described in Auto-Encoding Variational Bayes from ICLR 2014.
Randomly Sampled Images for 2D Latent Space
Latent Space
This model was trained to encode 784 dimensional MNIST images to just 2 dimensions and to then reconstruct it. The image below is a grid of outputs generated by walking through the 2D latent space Z.
Implementation Details
- The encoder and decoder are symmetrical MLPs with 256 neurons in each’s hidden layer.
- This implementation is inspired by Building Autoencoders in Keras.
- All the code was written and ran on Google Colab.
Requirements
torch
torchvision
numpy
matplotlib
References
- Diederik P. Kingma, et al. Auto-Encoding Variational Bayes ICLR 2014[arxiv]