Adversarially Learned Inference (ALI) is a generative modelling approach that casts the learning of both an inference machine (or encoder) and a deep directed generative model (or decoder) in an GANlike adversarial framework. A discriminator is trained to discriminate joint samples of the data and the corresponding latent variable from the encoder (or approximate posterior) from joint samples from the decoder while in opposition, the encoder and the decoder are trained together to fool the discriminator. Not is the discriminator asked to distinguish synthetic samples from real data, but it is required it to distinguish between two joint distributions over the data space and the latent variables.
An ALI differs from a GAN in two ways:
PAPER  DATE 

Generalized Adversarially Learned Inference
• • • 
20200615 
The InformationAutoencoding Family: A Lagrangian Perspective on Latent Variable Generative Modeling
• • 
20180101 
Adversarially Learned Inference
• • • • • • 
20160602 
TASK  PAPERS  SHARE 

Image Generation  1  50.00% 
ImagetoImage Translation  1  50.00% 
COMPONENT  TYPE 


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