Alternating Back-Propagation for Generator Network

Understand

This paper proposes an alternating back-propagation algorithm for learning the generator network model.

• The model is a non-linear generalization of factor analysis.
• In this model, the mapping from the continuous latent factors to the observed signal is parametrized by a convolutional neural network.
• The alternating back-propagation algorithm iterates the following two steps: (1) Inferential back-propagation, which infers the latent factors by Langevin dynamics or gradient descent.