Abstract
This paper proposes an alternating back-propagation algorithm for learning the generator network model. The model is a nonlinear 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. (2) Learning back-propagation, which updates the parameters given the inferred latent factors by gradient descent. The gradient computations in both steps are powered by back-propagation, and they share most of their code in common. We show that the alternating back-propagation algorithm can learn realistic generator models of natural images, video sequences, and sounds. Moreover, it can also be used to learn from incomplete or indirect training data.
Original language | English |
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Pages | 1976-1984 |
Number of pages | 9 |
State | Published - 2017 |
Event | 31st AAAI Conference on Artificial Intelligence, AAAI 2017 - San Francisco, United States Duration: 4 Feb 2017 → 10 Feb 2017 |
Conference
Conference | 31st AAAI Conference on Artificial Intelligence, AAAI 2017 |
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Country/Territory | United States |
City | San Francisco |
Period | 4/02/17 → 10/02/17 |