Knowledge Distillation via Route Constrained Optimization
Motivation 已有的KD方法提升性能都是基于一个假设：teacher模型可以提供一种弱监督，且能被一个小student网络学习，但是实际上一个收敛的大网络表示空间很难被student学习，这会导致congruence loss很高 因此本文提出一种策略route constrained optimization，根据参数空间的route去选择teacher的参数，一步一步的指导studen
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相关文章
- 1. Knowledge Distillation via Route Constrained Optimization
- 2. Awesome Knowledge-Distillation
- 3. Knowledge Distillation
- 4. Knowledge Distillation 笔记
- 5. Regularizing Class-wise Predictions via Self-knowledge Distillation
- 6. Constrained Optimization
- 7. Tutorial: Knowledge Distillation
- 8. Similarity-Preserving Knowledge Distillation
- 9. 论文Relational Knowledge Distillation
- 10. Correlation Congruence for Knowledge Distillation