Adversarial Training in Multi-Exit Networks: Proposed NEO-KD Algorithm and Problem Setup by@textmodels
Adversarial Training in Multi-Exit Networks: Proposed NEO-KD Algorithm and Problem Setup
by Writings, Papers and Blogs on Text ModelsSeptember 30th, 2024
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This section outlines adversarial training for multi-exit networks, focusing on three attack methods: single, max-average, and average attacks. The generated adversarial examples target different submodels, but the correlation among submodels can increase adversarial transferability, which is a challenge in multi-exit network robustness.
Authors:
(1) Seokil Ham, KAIST;
(2) Jungwuk Park, KAIST;
(3) Dong-Jun Han, Purdue University;
(4) Jaekyun Moon, KAIST.
Table of Links
3. Proposed NEO-KD Algorithm and 3.1 Problem Setup: Adversarial Training in Multi-Exit Networks
4. Experiments and 4.1 Experimental Setup
4.2. Main Experimental Results
4.3. Ablation Studies and Discussions
5. Conclusion, Acknowledgement and References
B. Clean Test Accuracy and C. Adversarial Training via Average Attack
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
This paper is available on arxiv under CC 4.0 license.
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