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FCKDNet: A Feature Condensation Knowledge Distillation Network for Semantic Segmentation.

Wenhao Yuan1, Xiaoyan Lu1, Rongfen Zhang1

  • 1College of Big Data and Information Engineering, Guizhou University, Guiyang 550025, China.

Entropy (Basel, Switzerland)
|January 21, 2023
PubMed
Summary

This study introduces a feature condensation method to improve knowledge distillation (KD) for semantic segmentation (SS). The proposed FCKDNet enhances teacher network features, reducing noise and boosting student model performance.

Keywords:
feature condensationfeature soft enhancementknowledge distillationprediction information entropysemantic segmentation

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Area of Science:

  • Computer Vision
  • Machine Learning

Background:

  • Knowledge distillation (KD) is crucial for semantic segmentation (SS) in computer vision.
  • Current KD methods are limited by the quality of feature knowledge from teacher networks.

Purpose of the Study:

  • To propose a novel feature condensation-based knowledge distillation network (FCKDNet).
  • To reduce pseudo-knowledge transfer and improve feature representation in teacher-student networks for semantic segmentation.

Main Methods:

  • Developed a feature condensation method using pixel information entropy to separate foreground features from background noise.
  • Applied a feature condensation matrix to teacher and student network outputs to enhance feature representation.
  • Introduced a soft feature enhancement method across spatial and channel dimensions.
  • Implemented separate distillation loss calculations for spatial and channel condensation features.

Main Results:

  • FCKDNet improved baseline performance by 3.16% (mAcc) on Pascal VOC and 2.98% (mAcc) on Cityscapes.
  • Achieved 2.03% (mIoU) and 2.30% (mIoU) improvements on Pascal VOC and Cityscapes, respectively.
  • Demonstrated superior segmentation performance and robustness compared to mainstream methods.

Conclusions:

  • The proposed feature condensation technique effectively mitigates pseudo-knowledge transfer in KD for SS.
  • FCKDNet enhances feature representation and accelerates student network convergence.
  • The method offers improved segmentation accuracy and robustness in computer vision applications.