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Online knowledge distillation optimization based on Multi-Student model Multi-Task collaborative learning
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DOI：10.1016/j.knosys.2026.115910.png)
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Abstract
En 中文
• The MTCL framework is proposed to address representation homogeneity via collaborative learning. • Heterogeneous auxiliary tasks are introduced to construct complementary Geometric and Semantic Experts. • A Collaborative Processing Module (CPM) integrates diverse features without rigid alignment. • The method achieves a significant 11.19% accuracy gain on TinyImageNet using ResNet-34. • Cost-effectiveness analysis demonstrates a high return on investment (ROI) with standard budgets.
Keywords:
Multi-Task Learning
Collaborative Learning
Knowledge Distillation
Representation Homogeneity
Cost-Effectiveness
Journal
K
IF:
7.6
Papers:
1.3W
Citations:
4.5W
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