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Differentiable Feature Aggregation Search for Knowledge Distillation

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 12362)

Abstract

Knowledge distillation has become increasingly important in model compression. It boosts the performance of a miniaturized student network with the supervision of the output distribution and feature maps from a sophisticated teacher network. Some recent works introduce multi-teacher distillation to provide more supervision to the student network. However, the effectiveness of multi-teacher distillation methods are accompanied by costly computation resources. To tackle with both the efficiency and the effectiveness of knowledge distillation, we introduce the feature aggregation to imitate the multi-teacher distillation in the single-teacher distillation framework by extracting informative supervision from multiple teacher feature maps. Specifically, we introduce DFA, a two-stage Differentiable Feature Aggregation search method that motivated by DARTS in neural architecture search, to efficiently find the aggregations. In the first stage, DFA formulates the searching problem as a bi-level optimization and leverages a novel bridge loss, which consists of a student-to-teacher path and a teacher-to-student path, to find appropriate feature aggregations. The two paths act as two players against each other, trying to optimize the unified architecture parameters to the opposite directions while guaranteeing both expressivity and learnability of the feature aggregation simultaneously. In the second stage, DFA performs knowledge distillation with the derived feature aggregation. Experimental results show that DFA outperforms existing distillation methods on CIFAR-100 and CINIC-10 datasets under various teacher-student settings, verifying the effectiveness and robustness of the design.

Keywords

Knowledge distillation Feature aggregation Differentiable architecture search 

Notes

Acknowledgment

This work is partially supported by National Key Research and Development Program No. 2017YFB0803302, Beijing Academy of Artificial Intelligence (BAAI), and NSFC 61632017.

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Peking UniversityBeijingChina
  2. 2.Megvii (Face++) Technology IncBeijingChina
  3. 3.National Engineering Laboratory for Big Data Analysis and ApplicationsBeijingChina
  4. 4.DiDi AI LabsBeijingChina

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