figure generative deep learning the smile vect or variational autoencoders variational autoencoderssimultaneously discovered by kingma and welling in december and rezendemohamedand wierstra in january , are kind of generative model that' especially appropriate for the task of image editing via concept vectors they're modern take on autoencoders-- type of network that aims to encode an input to low-dimensional latent space and then decode it back--that mixes ideas from deep learning with bayesian inference classical image autoencoder takes an imagemaps it to latent vector space via an encoder moduleand then decodes it back to an output with the same dimensions as the original imagevia decoder module (see figure it' then trained by using as target data the same images as the input imagesmeaning the autoencoder learns to reconstruct the original inputs by imposing various constraints on the code (the output of the encoder)you can get the autoencoder to learn more-or-less interesting latent representations of the data most commonlyyou'll constrain the code to be low-dimensional and sparse (mostly zeros)in which case the encoder acts as way to compress the input data into fewer bits of information diederik kingma and max welling"auto-encoding variational bayesarxiv ( )abs/ danilo jimenez rezendeshakir mohamedand daan wierstra"stochastic backpropagation and approximate inference in deep generative models,arxiv ( )licensed to