perturbation stability
DoWiderNeuralNetworksReallyHelpAdversarial Robustness?
Adversarial training is a powerful type of defense against adversarial examples.
Do Wider Neural Networks Really Help Adversarial Robustness?
Adversarial training is a powerful type of defense against adversarial examples. Previous empirical results suggest that adversarial training requires wider networks for better performances. However, it remains elusive how does neural network width affect model robustness. In this paper, we carefully examine the relationship between network width and model robustness. Specifically, we show that the model robustness is closely related to the tradeoff between natural accuracy and perturbation stability, which is controlled by the robust regularization parameter λ.
Clustering Stable Instances of Euclidean k-means.
Aravindan Vijayaraghavan, Abhratanu Dutta, Alex Wang
We design efficient algorithms that provably recover the optimal clustering for instances that are additive perturbation stable.
Appendix introduction
The Appendix is organized as follows: In Appendix A, we state the symbols and notation used in this paper. In Appendix B, we provide the proofs and related lemmas of Theorem 1. In Appendix C, we provide the proofs of Theorem 2. In Appendix D, we provide the proofs and related lemmas of Theorem 3. In Appendix F, we discuss several limitations of this work. Finally, in Appendix G, we discuss the societal impact of this paper. In the paper, vectors are indicated with bold small letters, matrices with bold capital letters.
Robustness in deep learning: The good (width), the bad (depth), and the ugly (initialization)
A plethora of aspects on the robustness have been studied, ranging from algorithms to their initialization as well as from the width of neural networks to their depth (i.e., the architecture).
Do Wider Neural Networks Really Help Adversarial Robustness?
Adversarial training is a powerful type of defense against adversarial examples. Previous empirical results suggest that adversarial training requires wider networks for better performances. However, it remains elusive how does neural network width affect model robustness. In this paper, we carefully examine the relationship between network width and model robustness. Specifically, we show that the model robustness is closely related to the tradeoff between natural accuracy and perturbation stability, which is controlled by the robust regularization parameter λ.