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• W2649558613 abstract "Leveraging upon transfer learning, we distill the knowledge in a conventional wide and deep neural network (DNN) into a narrower yet deeper model with fewer parameters and comparable system performance for speech enhancement. We present three transfer-learning solutions to accomplish our goal. First, the knowledge embedded in the form of the output values of a high-performance DNN is used to guide the training of a smaller DNN model in sequential transfer learning. In the second multi-task transfer learning solution, the smaller DNN is trained to learn the output value of the larger DNN, and the speech enhancement task in parallel. Finally, a progressive stacking transfer learning is accomplished through multi-task learning, and DNN stacking. Our experimental evidences demonstrate 5 times parameter reduction while maintaining similar enhancement performance with the proposed framework." @default.
• W2649558613 created "2017-06-30" @default.
• W2649558613 creator A5011008665 @default.
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• W2649558613 date "2017-03-01" @default.
• W2649558613 modified "2023-09-26" @default.
• W2649558613 title "A transfer learning and progressive stacking approach to reducing deep model sizes with an application to speech enhancement" @default.
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• W2649558613 doi "https://doi.org/10.1109/icassp.2017.7953223" @default.
• W2649558613 hasPublicationYear "2017" @default.
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