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Class-incremental learning with Balanced Embedding Discrimination Maximization.

Qinglai Wei1, Weiqin Zhang2

  • 1State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100049, China; Institute of Systems Engineering, Macau University of Science and Technology, 999078, Macao Special Administrative Region of China.

Neural Networks : the Official Journal of the International Neural Network Society
|July 10, 2024
PubMed
Summary
This summary is machine-generated.

Balanced Embedding Discrimination Maximization (BEDM) enhances class incremental learning by creating distinct embeddings and adapting to data imbalances. This method effectively combats catastrophic forgetting and improves classifier performance on new categories.

Keywords:
Bias mitigationClass incremental learningFeature independenceOrthogonality

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Machine Learning

Background:

  • Class incremental learning addresses sequential category additions while preventing catastrophic forgetting.
  • Existing methods struggle with imbalanced data and distribution shifts in incremental learning scenarios.

Purpose of the Study:

  • To develop a unified method, Balanced Embedding Discrimination Maximization (BEDM), for robust class incremental learning.
  • To enhance intermediate embedding distinctiveness and mitigate classifier bias caused by data imbalance.

Main Methods:

  • Utilizing an orthogonality constraint with doubly-blocked Toeplitz matrices to reduce kernel correlation.
  • Implementing adaptive balance weighting in softmax for dynamic compensation of insufficient categories.
  • Introducing hybrid embedding learning for efficient knowledge preservation from previous models.

Main Results:

  • BEDM outperforms existing approaches on three benchmark datasets.
  • Technical visualizations demonstrate a more uniform similarity histogram and stable spectrum.
  • Grad-CAM and t-SNE visualizations confirm the method's effectiveness in improving representation learning.

Conclusions:

  • BEDM offers a unified and effective solution for class incremental learning.
  • The proposed method successfully addresses challenges of embedding distinctiveness, data imbalance, and knowledge preservation.
  • BEDM demonstrates superior performance and improved model stability in incremental learning settings.