This content originally appeared on HackerNoon and was authored by Writings, Papers and Blogs on Text Models
:::info 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
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
E Discussions on Performance Degradation at Later Exits
As can be seen from the results for the anytime prediction in the main manuscript, the adversarial test accuracy of the later exits is sometimes lower than the performance of earlier exits. This phenomenon can be explained as follows: In general, we observed via experiments that adversarial examples targeting later exits has the higher sum of losses from all exits compared to adversarial examples targeting earlier exits. This makes max-average or average attack mainly focus on attacking the later exits, leading to low adversarial test accuracy at later exits. The performance of later exits can be improved by adopting the ensemble strategy as in the main manuscript for the budgeted prediction setup.
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:::info This paper is available on arxiv under CC 4.0 license.
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This content originally appeared on HackerNoon and was authored by Writings, Papers and Blogs on Text Models
Writings, Papers and Blogs on Text Models | Sciencx (2024-09-30T01:22:48+00:00) Examining the Adversarial Test Accuracy of Later Exits in NEO-KD Networks. Retrieved from https://www.scien.cx/2024/09/30/examining-the-adversarial-test-accuracy-of-later-exits-in-neo-kd-networks/
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