Search Results for author: Sean McPherson

Found 4 papers, 0 papers with code

An Analysis of RF Transfer Learning Behavior Using Synthetic Data

no code implementations3 Oct 2022 Lauren J. Wong, Sean McPherson, Alan J. Michaels

Transfer learning (TL) techniques, which leverage prior knowledge gained from data with different distributions to achieve higher performance and reduced training time, are often used in computer vision (CV) and natural language processing (NLP), but have yet to be fully utilized in the field of radio frequency machine learning (RFML).

Domain Adaptation Transfer Learning

Assessing the Value of Transfer Learning Metrics for RF Domain Adaptation

no code implementations16 Jun 2022 Lauren J. Wong, Sean McPherson, Alan J. Michaels

The use of transfer learning (TL) techniques has become common practice in fields such as computer vision (CV) and natural language processing (NLP).

BIG-bench Machine Learning Domain Adaptation +1

Explainable Neural Network-based Modulation Classification via Concept Bottleneck Models

no code implementations4 Jan 2021 Lauren J. Wong, Sean McPherson

While RFML is expected to be a key enabler of future wireless standards, a significant challenge to the widespread adoption of RFML techniques is the lack of explainability in deep learning models.

Classification General Classification +1

SpaceNet MVOI: a Multi-View Overhead Imagery Dataset

no code implementations ICCV 2019 Nicholas Weir, David Lindenbaum, Alexei Bastidas, Adam Van Etten, Sean McPherson, Jacob Shermeyer, Varun Kumar, Hanlin Tang

To address this problem, we present an open source Multi-View Overhead Imagery dataset, termed SpaceNet MVOI, with 27 unique looks from a broad range of viewing angles (-32. 5 degrees to 54. 0 degrees).

object-detection Object Detection +1

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