Smart transfer learning from pre-trained networks: a case study for infrared classification

Bethany Allik; Nathan Schomer; Cory Miller

doi:10.1117/12.2663908

13 June 2023 Smart transfer learning from pre-trained networks: a case study for infrared classification

Bethany Allik, Nathan Schomer, Cory Miller

Proceedings Volume 12521, Automatic Target Recognition XXXIII; 125210L (2023) https://doi.org/10.1117/12.2663908
Event: SPIE Defense + Commercial Sensing, 2023, Orlando, Florida, United States

Abstract

For many applications, Neural Network (NN) training remains difficult due to lack of relevant data. Transfer learning is a technique used to combat this challenge by leveraging knowledge gained to solve one problem effectively and “transferring” this knowledge to solve a different ill-posed problem. In this paper we present an optimal transfer learning method for visible to infrared (IR) classification networks. First, principal component analysis (PCA) of collected target and background imagery across the visible and IR domain is performed. This analysis shows clear separation in the visible domain but weak separation of IR features in the last fully connected layers of pretrained VGG11. Our test data shows increased separation in the IR domain at the earlier layers in the features module of VGG11, indicating that improved transfer learning can be accomplished by either retraining from this point, or by generating a new classifier with the earlier NN generated features . In this paper we first gain insights from PCA on intermediate layers of VGG11 to observe statistic separation of our data. From there, we learn new classifiers using the NN extracted features and show increased accuracy when optimal representative layers from VGG11 are used.

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Bethany Allik, Nathan Schomer, and Cory Miller "Smart transfer learning from pre-trained networks: a case study for infrared classification", Proc. SPIE 12521, Automatic Target Recognition XXXIII, 125210L (13 June 2023); https://doi.org/10.1117/12.2663908

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