Transfer Learning for Aided Target Recognition: Comparing Deep Learning to other Machine Learning Approaches

TL;DR

This paper compares deep learning and traditional machine learning approaches in transfer learning for aided target recognition, analyzing their effectiveness across various datasets and discussing future research directions.

Contribution

It provides an empirical comparison of DL and ML transfer learning methods in AiTR and discusses their limitations and assumptions.

Findings

Deep learning offers higher accuracy in transfer tasks.

Traditional ML approaches perform competitively in some scenarios.

Transfer learning effectiveness varies across datasets and methods.

Abstract

Aided target recognition (AiTR), the problem of classifying objects from sensor data, is an important problem with applications across industry and defense. While classification algorithms continue to improve, they often require more training data than is available or they do not transfer well to settings not represented in the training set. These problems are mitigated by transfer learning (TL), where knowledge gained in a well-understood source domain is transferred to a target domain of interest. In this context, the target domain could represents a poorly-labeled dataset, a different sensor, or an altogether new set of classes to identify. While TL for classification has been an active area of machine learning (ML) research for decades, transfer learning within a deep learning framework remains a relatively new area of research. Although deep learning (DL) provides exceptional modeling flexibility and accuracy on recent real world problems, open questions remain regarding how much transfer benefit is gained by using DL versus other ML architectures. Our goal is to address this shortcoming by comparing transfer learning within a DL framework to other ML approaches across transfer tasks and datasets. Our main contributions are: 1) an empirical analysis of DL and ML algorithms on several transfer tasks and domains including gene expressions and satellite imagery, and 2) a discussion of the limitations and assumptions of TL for aided target recognition -- both for DL and ML in general. We close with a discussion of future directions for DL transfer.