Doing more with less: similarity neural nets and metrics for small class imbalanced data sets

Charlie Veal; Jeffrey Schulz; Andrew Buck; Derek Anderson; James Keller; Mihail Popescu; Grant Scott; Dominic Ho; Timothy Wilkin

doi:10.1117/12.2558092

24 April 2020 Doing more with less: similarity neural nets and metrics for small class imbalanced data sets

Charlie Veal, Jeffrey Schulz, Andrew Buck, Derek Anderson, James Keller, Mihail Popescu, Grant Scott, Dominic Ho, Timothy Wilkin

Author Affiliations +

Proceedings Volume 11418, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XXV; 1141802 (2020) https://doi.org/10.1117/12.2558092
Event: SPIE Defense + Commercial Sensing, 2020, Online Only

Abstract

The focus of this article is deep learning on small, class imbalanced data sets in support of explosive hazard detection (EHD) and automatic target recognition (ATR). To this end, we explore artificial neural networks that are driven by similarity versus classification or regression. Similarity can be emphasized via network design, e.g., siamese networks, and/or underlying metric, e.g., contrastive or triple loss. The general goal of a similarity neural network (SNN) is discriminative training via focusing on similarity between tuples of like (and unlike) inputs. As such, SNNs have the potential to learn improved solutions on small data; aka do more with less". Herein, we explore different avenues and we show that SNNs are essentially neural feature extractors followed by k-nearest neighbor classification. Instead of experimenting on a government data set that cannot be shared, we instead focus on benchmark community data sets for sake of reproducible research. Preliminary findings are encouraging as they suggest that SNNs have great potential for tasks like EHD and ATR.

Conference Presentation

Citation Download Citation

Charlie Veal, Jeffrey Schulz, Andrew Buck, Derek Anderson, James Keller, Mihail Popescu, Grant Scott, Dominic Ho, and Timothy Wilkin "Doing more with less: similarity neural nets and metrics for small class imbalanced data sets", Proc. SPIE 11418, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XXV, 1141802 (24 April 2020); https://doi.org/10.1117/12.2558092

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available