Intelligent synthesis driven model calibration: framework and face recognition application

Conference Paper

Deep Neural Networks (DNNs) that achieve state-of-the-art results are still prone to suffer performance degradation when deployed in many real-world scenarios due to shifts between the training and deployment domains. Limited data from a given setting can be enriched through synthesis, then used to calibrate a pre-trained DNN to improve the performance in the setting. Most enrichment approaches try to generate as much data as possible; however, this blind approach is computationally expensive and can lead to generating redundant data. Contrary to this, we develop synthesis, here exemplified for faces, methods and propose information-driven approaches to exploit and optimally select face synthesis types both at training and testing. We show that our approaches, without re-designing a new DNN, lead to more efficient training and improved performance. We demonstrate the effectiveness of our approaches by calibrating a state-of-the-art DNN to two challenging face recognition datasets.

Full Text

Duke Authors

Cited Authors

  • Qiu, Q; Hashemi, J; Sapiro, G

Published Date

  • July 1, 2017

Published In

  • Proceedings 2017 Ieee International Conference on Computer Vision Workshops, Iccvw 2017

Volume / Issue

  • 2018-January /

Start / End Page

  • 2564 - 2572

International Standard Book Number 13 (ISBN-13)

  • 9781538610343

Digital Object Identifier (DOI)

  • 10.1109/ICCVW.2017.301

Citation Source

  • Scopus