A Tutorial on Variational Autoencoders with a Concise Keras Implementation | Louis Tiao
We give an in-depth practical guide to variational autoencoders from a probabilistic perspective.
Tutorial - What is a variational autoencoder? – Jaan Lı 李
jaan.ioUnderstanding Variational Autoencoders (VAEs) from two perspectives: deep learning and graphical models.
Generating music in the waveform domain
This is a write-up of a presentation on generating music in the waveform domain, which was part of a tutorial that I co-presented at ISMIR 2019 earlier this month.
Generating music in the waveform domain
This is a write-up of a presentation on generating music in the waveform domain, which was part of a tutorial that I co-presented at ISMIR 2019 earlier this month.
A Practical guide to Autoencoders using Keras - reckoning.dev
An autoencoder is a type of artificial neural network used to learn efficient codings of unlabeled data. An autoencoder learns two functions: an encoding function that transforms the input data, and a decoding function that recreates the input data from the encoded representation.
From Autoencoder to Beta-VAE
[Updated on 2019-07-18: add a section on VQ-VAE & VQ-VAE-2.] [Updated on 2019-07-26: add a section on TD-VAE.] Autocoder is invented to reconstruct high-dimensional data using a neural network model with a narrow bottleneck layer in the middle (oops, this is probably not true for Variational Autoencoder, and we will investigate it in details in later sections). A nice byproduct is dimension reduction: the bottleneck layer captures a compressed latent encoding. Such a low-dimensional representation can be used as en embedding vector in various applications (i.e. search), help data compression, or reveal the underlying data generative factors.
GitHub - jaanli/variational-autoencoder: Variational autoencoder implemented in tensorflow and pytorch (including inverse autoregressive flow)
Variational autoencoder implemented in tensorflow and pytorch (including inverse autoregressive flow) - jaanli/variational-autoencoder
Variational Autoencoder in PyTorch, commented and annotated.
I have recently become fascinated with (Variational) Autoencoders and with PyTorch. Kevin Frans has a beautiful blog post online explaining variational autoencoders, with examples in TensorFlow and, importantly, with cat pictures. Jaan Altosaar’s blog post takes an even deeper look at VAEs from both the deep learning perspective and the perspective of graphical models. Both of these posts, as well as Diederik Kingma’s original 2014 paper Auto-Encoding Variational Bayes, are more than worth your time.
Deep Learning: Variational Auto-Encoders
Variational Auto-Encoders (VAE) are a probabilistic (variational) extension of classical auto-encoders. Disclaimer: This article does not approach the concept o