Differentiable Feature Aggregation Search for Knowledge Distillation

TL;DR

This paper introduces DFA, a differentiable search method for optimal feature aggregation in knowledge distillation, improving efficiency and performance by mimicking multi-teacher supervision within a single-teacher framework.

Contribution

DFA employs a bi-level optimization with a novel bridge loss to automatically discover effective feature aggregations for knowledge distillation, reducing computational costs.

Findings

DFA outperforms existing methods on CIFAR-100 and CINIC-10 datasets.

The method effectively mimics multi-teacher distillation with a single teacher.

DFA demonstrates robustness across various teacher-student configurations.

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 methods on CIFAR-100 and CINIC-10 datasets under various teacher-student settings, verifying the effectiveness and robustness of the design.