| Title | Heterogeneous Transfer in Deep Learning for Spectrogram Classification in Cognitive Communications |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Cody, Tyler, Beling, Peter A. |
| Conference Name | 2021 IEEE Cognitive Communications for Aerospace Applications Workshop (CCAAW) |
| Keywords | Cognitive radio, Cognitive Radio Security, Cognitive Radios, Conferences, Deep Learning, pubcrawl, resilience, Resiliency, Sensitivity, Spectrogram, Training, transfer learning |
| Abstract | Machine learning offers performance improvements and novel functionality, but its life cycle performance is understudied. In areas like cognitive communications, where systems are long-lived, life cycle trade-offs are key to system design. Herein, we consider the use of deep learning to classify spectrograms. We vary the label-space over which the network makes classifications, as may emerge with changes in use over a system's life cycle, and compare heterogeneous transfer learning performance across label-spaces between model architectures. Our results offer an empirical example of life cycle challenges to using machine learning for cognitive communications. They evidence important trade-offs among performance, training time, and sensitivity to the order in which the label-space is changed. And they show that fine-tuning can be used in the heterogeneous transfer of spectrogram classifiers. |
| DOI | 10.1109/CCAAW50069.2021.9527298 |
| Citation Key | cody_heterogeneous_2021 |
Heterogeneous Transfer in Deep Learning for Spectrogram Classification in Cognitive Communications