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Research on a Cross-Domain Few-Shot Adaptive Classification Algorithm Based on Knowledge Distillation Technology.

Jiuyang Gao1, Siyu Li2, Wenfeng Xia1

  • 1Hubei Provincial Engineering Technology Research Center of Green Chemical Equipment, School of Mechanical and Electrical Engineering, Wuhan Institute of Technology, Wuhan 430205, China.

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|March 28, 2024
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Summary
This summary is machine-generated.

This study introduces a Self-Distillation and Mixing (SDM) method to improve deep learning models with limited data. SDM enhances cross-domain learning for computer vision inspection, achieving better accuracy and faster training times.

Keywords:
SDMfew-shotmeta-learningself-distillation

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Area of Science:

  • Computer Vision
  • Deep Learning
  • Machine Learning

Background:

  • Deep learning models require extensive data for generalization, posing challenges due to privacy, cost, and sensor limitations.
  • Domain offset between training and real-world scenarios hinders model performance in computer vision inspection.
  • Meta-learning shows promise for few-shot learning but is limited by small target domain datasets.

Purpose of the Study:

  • To develop an effective method for knowledge transfer in deep learning for computer vision inspection with limited target domain samples.
  • To address the challenge of domain offset and improve model generalization in practical deployment scenarios.
  • To enhance the utilization of limited samples for training robust computer vision models.

Main Methods:

  • A novel Self-Distillation and Mixing (SDM) method was developed using a Teacher-Student framework.
  • The SDM method employs self-distillation techniques and mixed data augmentation for knowledge transfer.
  • It focuses on learning improved image representations from abundant datasets and fine-tuning on the target domain.

Main Results:

  • The SDM method demonstrated superior performance compared to nine classical models in terms of training time and accuracy.
  • Experimental results confirm effective knowledge transfer from source to target domains, even with limited target samples.
  • The approach successfully mitigates issues related to limited data and domain offset in computer vision tasks.

Conclusions:

  • The Self-Distillation and Mixing (SDM) method offers a robust solution for few-shot learning and cross-domain knowledge transfer in computer vision.
  • SDM significantly improves model efficiency and accuracy, making it suitable for practical applications with data constraints.
  • This approach advances the capabilities of deep learning in computer vision inspection by overcoming common deployment challenges.