Active Recall Networks for Multiperspectivity Learning through Shared Latent Space Optimization

Author(s)

Theus Aspiras, University of DaytonFollow
Ruixu Liu, University of Dayton
Vijayan K. Asari, University of DaytonFollow

Document Type

Article

Publication Date

2019

Publication Source

IJCCI: Proceedings of the 11th International Joint Conference on Computational Intelligence

Abstract

Given that there are numerous amounts of unlabeled data available for usage in training neural networks, it is desirable to implement a neural network architecture and training paradigm to maximize the ability of the latent space representation. Through multiple perspectives of the latent space using adversarial learning and autoencoding, data requirements can be reduced, which improves learning ability across domains. The entire goal of the proposed work is not to train exhaustively, but to train with multiperspectivity. We propose a new neural network architecture called Active Recall Network (ARN) for learning with less labels by optimizing the latent space. This neural network architecture learns latent space features of unlabeled data by using a fusion framework of an autoencoder and a generative adversarial network. Variations in the latent space representations will be captured and modeled by generation, discrimination, and reconstruction strategies in the network using both unlabeled and labeled data. Performance evaluations conducted on the proposed ARN architectures with two popular datasets demonstrated promising results in terms of generative capabilities and latent space effectiveness. Through the multiple perspectives that are embedded in ARN, we envision that this architecture will be incredibly versatile in every application that requires learning with less labels.

Inclusive pages

434-443

ISBN/ISSN

978-989-758-384-1

Document Version

Published Version

Comments

This open-access article is provided for download in compliance with the publisher’s policy on self-archiving. To view the version of record, use the DOI: https://doi.org/10.5220/0008369304340443

Publisher

SciTe Press

Peer Reviewed

yes

eCommons Citation

Aspiras, Theus; Liu, Ruixu; and Asari, Vijayan K., "Active Recall Networks for Multiperspectivity Learning through Shared Latent Space Optimization" (2019). Electrical and Computer Engineering Faculty Publications. 462.
https://ecommons.udayton.edu/ece_fac_pub/462