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Updated: Nov 16, 2025

Foreign Accent and Forensic Speaker Identification in Voice Lineups: The Influence of Acoustic Features Based on Prosody
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Towards effective deep transfer via attentive feature alignment.

Zheng Xie1, Zhiquan Wen1, Yaowei Wang2

  • 1South China University of Technology, China; PengCheng Laboratory, China.

Neural Networks : the Official Journal of the International Neural Network Society
|February 26, 2021
PubMed
Summary
This summary is machine-generated.

Attentive Feature Alignment (AFA) improves deep learning by focusing on relevant features for domain adaptation. This method enhances knowledge transfer from source to target domains, boosting performance on new tasks.

Keywords:
Attention mechanismDeep transferKnowledge distillation

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Machine Learning

Background:

  • Deep convolutional networks require extensive labeled data for training from scratch.
  • Adapting pre-trained models across domains is challenging due to differing data distributions.

Purpose of the Study:

  • To propose an effective domain knowledge transfer method for deep learning.
  • To address limitations in existing feature alignment techniques for domain adaptation.

Main Methods:

  • Introduced Attentive Feature Alignment (AFA) using channel- and spatial-level attentive modules.
  • Implemented sequential attentive spatial- and channel-level feature alignments.
  • Simultaneously learned the target model and attentive modules.

Main Results:

  • Theoretically analyzed and confirmed the generalization performance of AFA.
  • Demonstrated superior performance compared to existing methods through extensive experiments.
  • Achieved effective domain knowledge transfer by identifying and attending to relevant features.

Conclusions:

  • Attentive Feature Alignment (AFA) offers a superior approach to domain adaptation in deep learning.
  • The method effectively transfers knowledge by focusing on domain-relevant features.
  • AFA shows significant effectiveness in image classification and face recognition tasks.