201. Interpolated Adversarial Training: Achieving Robust Neural Networks Without Sacrificing Too Much Accuracy
- Author
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Juho Kannala, Yoshua Bengio, Alex Lamb, Vikas Verma, Professorship Kannala Juho, University of Montreal, Department of Computer Science, Aalto-yliopisto, and Aalto University
- Subjects
TheoryofComputation_MISCELLANEOUS ,Artificial neural network ,Computer science ,business.industry ,Deep learning ,Adversary ,Training methods ,Machine learning ,computer.software_genre ,Adversarial system ,Standard error ,Robustness (computer science) ,Artificial intelligence ,business ,computer ,Interpolation - Abstract
Adversarial robustness has become a central goal in deep learning, both in theory and in practice. However, successful methods to improve the adversarial robustness (such as adversarial training) greatly hurt generalization performance on the unperturbed data. This could have a major impact on how achieving adversarial robustness affects real world systems (i.e. many may opt to forego robustness if it can improve accuracy on the unperturbed data). We propose Interpolated Adversarial Training, which employs recently proposed interpolation based training methods in the framework of adversarial training. On CIFAR-10, adversarial training increases the standard test error (when there is no adversary) from 4.43% to 12.32%, whereas with our Interpolated adversarial training we retain adversarial robustness while achieving a standard test error of only 6.45%. With our technique, the relative increase in the standard error for the robust model is reduced from 178.1% to just 45.5%.
- Published
- 2019