A rekindled the interest in auto-encoder algorithms has been spurred by recent work on deep learning. Current efforts have been directed towards effective training of auto-encoder architectures with a large number of coding units. Here, we propose a learning algorithm for auto-encoders based on a rate-distortion objective that minimizes the mutual information between the inputs and the outputs of the auto-encoder subject to a fidelity constraint. The goal is to learn a representation that is minimally committed to the input data, but that is rich enough to reconstruct the inputs up to certain level of distortion. Minimizing the mutual information acts as a regularization term whereas the fidelity constraint can be understood as a risk functional in the conventional statistical learning setting. The proposed algorithm uses a recently introduced measure of entropy based on infinitely divisible matrices that avoids the plug in estimation of densities. Experiments using over-complete bases show that the rate-distortion auto-encoders can learn a regularized input-output mapping in an implicit manner.
|State||Published - 2014|
|Event||2nd International Conference on Learning Representations, ICLR 2014 - Banff, Canada|
Duration: Apr 14 2014 → Apr 16 2014
|Conference||2nd International Conference on Learning Representations, ICLR 2014|
|Period||4/14/14 → 4/16/14|
Bibliographical notePublisher Copyright:
© 2014 International Conference on Learning Representations, ICLR. All rights reserved.
ASJC Scopus subject areas
- Computer Science Applications
- Linguistics and Language
- Language and Linguistics