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.


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    Title :

    Heterogeneous Transfer in Deep Learning for Spectrogram Classification in Cognitive Communications


    Contributors:


    Publication date :

    2021-06-21


    Size :

    4030411 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English





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