AutoEncoders in PyTorch

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Description

This repo contains an implementation of the following AutoEncoders:

• Vanilla AutoEncoders - AE:
  The most basic autoencoder structure is one which simply maps input data-points through a bottleneck layer whose dimensionality is smaller than the input.

• Variational AutoEncoders - VAE:
  The Variational Autoencoder introduces the constraint that the latent code z is a random variable distributed according to a prior distribution p(z).

• Adversarially Constrained Autoencoder Interpolations - ACAI:
  A critic network tries to predict the interpolation coefficient α corresponding to an interpolated datapoint. The autoencoder is trained to fool the critic into outputting α = 0.
  

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Setup

Create a Python Virtual Environment

mkvirtualenv --python=/usr/bin/python3 pytorch-AE

Install dependencies

pip install torch torchvision

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Training

python train.py --help

Training Options and some examples:

• Vanilla Autoencoder:

  python train.py --model AE
  

• Variational Autoencoder:

  python train.py --model VAE --batch-size 512 --dataset EMNIST --seed 42 --log-interval 500 --epochs 5 --embedding-size 128
  

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Results

Vanilla AutoEncoders	Variational AutoEncoders	ACAI

Contributing:

If you have suggestions or any type of contribution idea, file an issue, make a PR and don't forget to star the repository

More projects:

Feel free to check out my other repos with more work in Machine Learning:

• World Models in TensorFlow
• TensorBlob
• banaNavigation