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Updated: Jun 27, 2025

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
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Learning label smoothing for text classification.

Han Ren1,2, Yajie Zhao3, Yong Zhang4

  • 1Laboratory of Language Engineering and Computing, Guangdong University of Foreign Studies, Guangzhou, China.

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|April 30, 2024
PubMed
Summary
This summary is machine-generated.

Discrimination-aware label smoothing improves deep learning models by adaptively distributing soft labels. This method enhances model robustness and generalization in text classification tasks.

Keywords:
Excessive regularizationLabel smoothingNeural networkSoft labelText classification

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Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Natural Language Processing

Background:

  • Deep learning models benefit from soft labels over hard labels for improved robustness and generalization.
  • Standard label smoothing assigns uniform soft labels, ignoring semantic label differences.
  • Existing methods lack adaptability to label semantics during training.

Purpose of the Study:

  • Introduce discrimination-aware label smoothing, an adaptive approach for iterative optimization.
  • Enhance model regularization and calibration by considering label semantics.
  • Improve deep learning model performance in text classification.

Main Methods:

  • Employing positive and negative samples to inform label distribution.
  • Developing an iterative learning method for adaptive soft label generation.
  • Integrating discrimination-aware label smoothing into deep learning training.

Main Results:

  • Demonstrated effectiveness across five diverse text classification datasets.
  • Showcased significant improvements in model robustness and generalization.
  • Validated the benefits of adaptive soft label distribution over uniform methods.

Conclusions:

  • Discrimination-aware label smoothing offers a more nuanced approach to training deep learning models.
  • The method effectively leverages label semantics for enhanced performance.
  • This adaptive strategy represents a promising advancement in robust deep learning.