Variational Auto Encoder models tend to make strong assumptions related to the distribution of latent variables. They use a variational approach for latent representation learning, which results in an additional loss component and a specific estimator for the training algorithm called the Stochastic Gradient Variational Bayes estimator. The probability distribution of the latent vector of a variational autoencoder typically matches the training data much closer than a standard autoencoder. As VAE are much more flexible and customisable in their generation behaviour than GANs, they are suitable for art generation of any kind.
python3 sample_keras.py
python3 sample_pytorch.py
- https://reyhaneaskari.github.io/AE.htm
- https://medium.com/@miguelmendez_/vaes-i-generating-images-with-tensorflow-f81b2f1c63b0
- https://github.com/FaustineLi/Variational-Autoencoders
- https://towardsdatascience.com/intuitively-understanding-variational-autoencoders-1bfe67eb5daf
- https://towardsdatascience.com/understanding-variational-autoencoders-vaes-f70510919f73
- https://towardsdatascience.com/variational-autoencoders-as-generative-models-with-keras-e0c79415a7eb (use incognito)
- https://keras.io/examples/generative/vae/
- https://www.tensorflow.org/tutorials/generative/cvae
- https://ml.informatik.uni-freiburg.de/papers/15-NIPS-auto-sklearn-preprint.pdf
- https://hanxiao.io/2018/06/24/4-Encoding-Blocks-You-Need-to-Know-Besides-LSTM-RNN-in-Tensorflow/
- https://www.mygreatlearning.com/blog/autoencoder/
- https://www.datacamp.com/community/tutorials/autoencoder-keras-tutorial
- https://pythonmachinelearning.pro/all-about-autoencoders/
- https://www.youtube.com/watch?v=YV9D3TWY5Zo