Conference Publication Details
Mandatory Fields
Peter Corcoran, Shabab Bazrafkan
Embedded Vision Summit 2018
Hybrid Semi-Parallel Deep Neural Networks (SPDNN) Example Methodologies & Use Cases
2018
May
Published
1
()
Optional Fields
Deep Learning Deep Neural Networks DNN Architecture DNN Methodology Biometrics
Jeff Bier
1
54
Santa Clara Convention Center, Santa Clara, California
23-MAY-18
23-MAY-18
Deep neural networks (DNNs) are typically trained on specific datasets, optimized with particular discriminating capabilities. Often several different DNN topologies are developed solving closely related aspects of a computer vision problem. But to utilize these topologies together, leveraging their individual discriminating capabilities, requires implementing each DNN separately, increasing the cost of practical solutions. In this talk, a methodology to merge multiple deep networks using graph contraction is developed. This provides a single network topology, achieving significant reduction in size over the individual networks. More significantly, this merged SPDNN network can be re-trained across the combined datasets used to train the original networks, improving its accuracy over the original networks. The result is a single network that is more generic, but with equivalent or often enhanced performance over a wider range of input data. Examples of several problems in contemporary computer vision are solved using SPDNNs. These include significantly improving segmentation accuracy of eye-iris regions (a key component of iris biometric authentication) and mapping depth from monocular images, demonstrating equivalent performance to stereo depth mapping.
science foundation Ireland
https://www.dropbox.com/s/y8x86lcqgch774e/__Xperi_Corcoran2_2018_Embedded_Vision_Summit_Slides_SPDNN02_MT_JB_PCv3_FINAL_JB.pdf?dl=0
Grant Details
13/SPP/12868 Next Generation Imaging for Smartphones & Embedded Imaging Devices
Publication Themes
Informatics, Physical and Computational Sciences