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April 3, 2026AlgorithmsOpen Access

Accelerating Realization of Effective Capacity in Lightweight Vision Models via Self-Competitive Distillation

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Authors

WZWeidong ZhangBLBing LiHLHuan Liu

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Overview

Self-Competitive Distillation enhances test accuracy in lightweight models, suggesting effective training dynamics and improved performance.

Key Points

  • The aim is to improve the optimization dynamics of lightweight vision models without increasing their size or complexity.
  • Introduced Self-Competitive Distillation (SCD) as a training strategy.
  • Trained two identical instances of a model with different initial random seeds.
  • Enabled dynamic exchange of teacher-student roles based on performance.
  • Monitored performance across multiple lightweight vision models and datasets.
  • Observed higher test accuracy at matched training epochs.
  • Improved in-domain performance across various datasets.
  • Achieved enhanced cross-domain generalization as indicated by xScore.

Cite This Study

Zhang et al. (2026) studied this question.

synapsesocial.com/papers/69cf5ecb5a333a821460d7c5https://doi.org/10.3390/a19040262
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