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Deep Transfer Learning Across Domains, Modalities and Classes

Award Information
Agency: Department of Defense
Branch: Air Force
Contract: FA8649-20-P-0352
Agency Tracking Number: F19C-004-0078
Amount: $150,000.00
Phase: Phase I
Program: STTR
Solicitation Topic Code: AF19C-T004
Solicitation Number: 19.C
Timeline
Solicitation Year: 2019
Award Year: 2020
Award Start Date (Proposal Award Date): 2019-12-12
Award End Date (Contract End Date): 2020-12-12
Small Business Information
20271 Goldenrod Lane Suite 2066
Germantown, MD 20876
United States
DUNS: 967349668
HUBZone Owned: No
Woman Owned: Yes
Socially and Economically Disadvantaged: No
Principal Investigator
 Hua-Mei Chen
 Principal Scientist
 (301) 515-7261
 huamei.chen@intfusiontech.com
Business Contact
 Yingli Wu
Phone: (949) 596-0057
Email: yingliwu@intfusiontech.com
Research Institution
 Rochester Institute of Technology
 Ryne Raffaelle Ryne Raffaelle
 
141 Lomb Memorial Drive
Rochester, NY 14623
United States

 (585) 475-2055
 Nonprofit College or University
Abstract

The capability of “transferring” learned classifiers from one domain, or set of targets, to classify different targets or the same targets but in different domains is of great interest to the United States Air Force. It is an enabling technology for the USAF to build Aided Target Recognition and other algorithms for environments and targets where the data or labeled data is scarce. In this project, Intelligent Fusion Technologies, Inc. and Rochester Institute of Technology propose a deep transfer learning approach to meet the USAF’s need. The proposed work is built upon the recent discriminative adversarial domain adaptation framework with the addition of one labeled image per target class. A key component of the proposed solution is a loss function that will guide the few labeled target features to move toward the targeted locations in the source domain. The proposed solution can be used in the following scenarios: i) transferring knowledge from simulated to measured data, ii) transferring from one modality (e.g., EO) to another (e.g., SAR), iii) transferring knowledge to new imaging conditions or sensors, and iv) transferring knowledge from one set of targets (e.g., sedan, SUV, pickup truck,…) to another set of targets (e.g., tank, LUV, military truck,…).

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

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