REAPER: A Framework for Materializing and Reusing Deep-Learning Models

REAPER: A Framework for Materializing and Reusing Deep-Learning Models

(Third Party Funds Group – Sub project)

Overall project: EFRE EIASY-Opt - Competence and Analysis Project for the "Data-driven Process and Production Optimization with the help of Data Mining and Big Data"
Project leader: Klaus Meyer-Wegener
Project members: Melanie Bianca Sigl, Luciano Melodia
Start date: 01/01/2017
End date: 31/12/2020
Acronym: E|ASY-Opt INF6
Funding source: Sonstige EU-Programme (z. B. RFCS, DG Health, IMI, Artemis), Bayerische Staatsministerien
URL: https://www.faps.fau.de/curforsch/efre-easy-opt/

Abstract

Within the framework of the EFRE-E|ASY-Opt subproject, the potential of data mining methods in the area of manufacturing is being investigated. Especially the training of Deep-Learning models is a computationally intensive task, which may take hours or several days. The training time can be shortened considerably by using an already trained model, as long as the goal and source task are closely related. This connection is not yet fully understood.

The aim of this research project is to implement a system called REAPER (Reusable Neural Network Pattern Repository) to support data scientists in storing and reusing already trained deep learning models.

Publications

• Sigl MB.:
  Don't Fear the REAPER: A Framework for Materializing and Reusing Deep-Learning Models
  International Conference on Data Engineering (Macau SAR, China, 08/04/2019 - 11/04/2019)
  DOI: 10.1109/ICDE.2019.00246
  BibTeX: Download