Learning Multiple Views with Orthogonal Denoising Autoencoders

By Tengqi Ye, Tianchun Wang, Kevin McGuinness, Yu Guo, Cathal Gurrin. 2016

20 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Francisco Herrera
2. Salvador Garcia
3. M. J. Jesús
4. Francisco Charte
5. Chongman Leong
6. Derek F. Wong
7. Lidia S. Chao
8. David Charte

In Computer Vision and Pattern Recognition

1. Salvador Garcia
2. Francisco Herrera
3. Chongman Leong
4. M. J. Jesús
5. Derek F. Wong
6. Lidia S. Chao
7. Francisco Charte
8. David Charte

Citations in the corpus

• A practical tutorial on autoencoders for nonlinear feature fusion: Taxonomy, models, software and guidelines (2018, methodology): “The authors of [113] present a general procedure named Orthogonal Autoencoder for Multi-View .”
• UM-pAligner: Neural Network-Based Parallel Sentence Identification Model (2018, methodology): “To maintain the orthogonality of the private spaces, the bias is disconnected from the private spaces (Ye et al., 2016).”