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Machine Learning for Transfer Learning for Periscopes

Award Information
Agency: Department of Defense
Branch: Navy
Contract: N68335-20-C-0788
Agency Tracking Number: N20A-T007-0275
Amount: $140,000.00
Phase: Phase I
Program: STTR
Solicitation Topic Code: N20A-T007
Solicitation Number: 20.A
Timeline
Solicitation Year: 2020
Award Year: 2020
Award Start Date (Proposal Award Date): 2020-08-08
Award End Date (Contract End Date): 2021-02-03
Small Business Information
9301 Corbin Avenue Suite 2000
Northridge, CA 91324-1111
United States
DUNS: 082191198
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Bradley Walls
 (520) 770-6062
 bwalls@arete.com
Business Contact
 Greg Fetzer
Phone: (303) 651-6756
Email: contractsx@arete.com
Research Institution
 University of Arizona
 Clayton Morrison
 
888 N Euclid Ave Room 510
Tucson, AZ 85719-4824
United States

 (520) 621-6609
 Nonprofit college or university
Abstract

Areté and the Machine Learning for Artificial Intelligence (MLAI) Lab at the University of Arizona (UofA) will develop and demonstrate new approaches that improve the performance of in situ machine learning (ML) algorithms as they evolve over time, adapt to new environments, and are capable of transferring their learned experiences across platforms.  Technological advances that will be brought to bear in this solution include: Meta-Learning, Deep Reinforcement Learning with Visual Attention, Grad-CAM, and Single/Few-Shot Learning. Areté’s solution addresses key problems facing many DoD ML implementations in using commercially developed DNNs –  the lack of sufficient, operationally relevant data to train DNN architectures, the need to quickly adapt to new and changing operational conditions, and the desire to shared “learned” experiences across platforms.  Areté’s approach brings three innovations that tackle these criticisms: A CNN capable of vessel detection, vessel identification, and vessel re-acquisition trained using a model agnostic meta-learning paradigm. An “online” learning capability so that the CNN can learn in situ, adapting to changing situations using a network visualization technique and deep reinforcement learning with visual attention  A method for optimal transfer learning from more experienced networks to less experienced networks. Areté will develop this capability in suite of algorithms to as a software package that implements these innovations for transition to the Navy to improve on the tasks of timely vessel detecting, target classification, target re-acquisition using periscope imagery.

* Information listed above is at the time of submission. *

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