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Hamed Riazati Seresht, Dr. Karim Mohammadi,
Volume 10, Issue 1 (3-2023)
Abstract
Insufficient training data is one of the main challenges of utilizing deep Convolutional Neural Networks (CNNs) for Environmental Sound Classification (ESC). As a promising solution, Transfer Learning (TL) has addressed this issue by adapting a network pre-trained on a large-scale dataset to the target task. In this paper, we demonstrate that not all neurons/kernels of every layer in CNN networks are equally utilized to process the inputs of different classes, but there is a specific subgroups of neurons/kernels in every layer that play the key role in classification of every output class. Based on this observation and due to similarities that exist between feature spaces of some source and target classes, we propose to concentrate the fine-tuning process only on those neurons/kernels that do need changes and have the greatest impact on misclassifying target data. To identify these neurons/kernels, we pose a nested optimization problem for which we propose an effective evolutionary approach as solution. Compared to the conventional fine-tuning approach, our proposed method achieves absolute improvements of about 1.9% and 2.3% in accuracy on ESC-50 and DCASE-17, respectively; remarkable improvements produced not by adding augmented data but with a more efficient utilization of knowledge stored in the pre-trained network. It is noteworthy that the computation time overhead of the proposed evolutionary method is rather small (about one third of the time required to train the model from scratch.
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| نشریه سامانههای غیرخطی در مهندسی برق در خصوص اصول اخلاقی انتشار مقاله، از توصیههای «کمیته بینالمللی اخلاق نشر» موسوم به COPE و «منشور و موازین اخلاق پژوهش» مصوب معاونت پژوهش و فناوری وزارت علوم، تحقیقات و فناوری تبعیت میکند. |
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