Adversarial Training in Multi-Exit Networks: Proposed NEO-KD Algorithm and Problem Setup

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Authors:

(1) Seokil Ham, KAIST;

(2) Jungwuk Park, KAIST;

(3) Dong-Jun Han, Purdue University;

(4) Jaekyun Moon, KAIST.

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Table of Links

Abstract and 1. Introduction

2. Related Works

3. Proposed NEO-KD Algorithm and 3.1 Problem Setup: Adversarial Training in Multi-Exit Networks

3.2 Algorithm Description

4. Experiments and 4.1 Experimental Setup

4.2. Main Experimental Results

4.3. Ablation Studies and Discussions

5. Conclusion, Acknowledgement and References

A. Experiment Details

B. Clean Test Accuracy and C. Adversarial Training via Average Attack

D. Hyperparameter Tuning

E. Discussions on Performance Degradation at Later Exits

F. Comparison with Recent Defense Methods for Single-Exit Networks

G. Comparison with SKD and ARD and H. Implementations of Stronger Attacker Algorithms

3 Proposed NEO-KD Algorithm

3.1 Problem Setup: Adversarial Training in Multi-Exit Networks

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This paper is available on arxiv under CC 4.0 license.

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