Understanding Adversarial Training: Increasing Local Stability of Neural Nets through Robust OptimizationUnderstanding Adversarial Training: Increasing Local Stability of Neural Nets through Robust OptimizationUri Shaham and Yutaro Yamada and Sahand Negahban2015
Paper summarydavidstutzShaham et al. provide an interpretation of adversarial training in the context of robust optimization. In particular, adversarial training is posed as min-max problem (similar to other related work, as I found):
$\min_\theta \sum_i \max_{r \in U_i} J(\theta, x_i + r, y_i)$
where $U_i$ is called the uncertainty set corresponding to sample $x_i$ – in the context of adversarial examples, this might be an $\epsilon$-ball around the sample quantifying the maximum perturbation allowed; $(x_i, y_i)$ are training samples, $\theta$ the parameters and $J$ the trianing objective. In practice, when the overall minimization problem is tackled using gradient descent, the inner maximization problem cannot be solved exactly (as this would be inefficient). Instead Shaham et al. Propose to alternatingly make single steps both for the minimization and the maximization problems – in the spirit of generative adversarial network training.
Also find this summary at [davidstutz.de](https://davidstutz.de/category/reading/).
First published: 2015/11/17 (2 years ago) Abstract: We propose a general framework for increasing local stability of Artificial
Neural Nets (ANNs) using Robust Optimization (RO). We achieve this through an
alternating minimization-maximization procedure, in which the loss of the
network is minimized over perturbed examples that are generated at each
parameter update. We show that adversarial training of ANNs is in fact
robustification of the network optimization, and that our proposed framework
generalizes previous approaches for increasing local stability of ANNs.
Experimental results reveal that our approach increases the robustness of the
network to existing adversarial examples, while making it harder to generate
new ones. Furthermore, our algorithm improves the accuracy of the network also
on the original test data.
Shaham et al. provide an interpretation of adversarial training in the context of robust optimization. In particular, adversarial training is posed as min-max problem (similar to other related work, as I found):
$\min_\theta \sum_i \max_{r \in U_i} J(\theta, x_i + r, y_i)$
where $U_i$ is called the uncertainty set corresponding to sample $x_i$ – in the context of adversarial examples, this might be an $\epsilon$-ball around the sample quantifying the maximum perturbation allowed; $(x_i, y_i)$ are training samples, $\theta$ the parameters and $J$ the trianing objective. In practice, when the overall minimization problem is tackled using gradient descent, the inner maximization problem cannot be solved exactly (as this would be inefficient). Instead Shaham et al. Propose to alternatingly make single steps both for the minimization and the maximization problems – in the spirit of generative adversarial network training.
Also find this summary at [davidstutz.de](https://davidstutz.de/category/reading/).