… Using a regularization parameter, we can control the trade-… way as many adversarial processes
for machine learning [14, … Thus, we regularize machine learning models for privacy. Our …

… ‘consistency’ regularization loss, as a simpler and easy-to-use alternative for regularizing AT.
… an auxiliary consistency regularization loss during AT: it forces adversarial examples from …

… training, a method to train a network with iter FGSM adversarial images … adversarial training
regularized with a unified embedding for classification and low- level similarity learning by …

… machine learning models against adversarial examples. More specifically, using the unified
framework, we develop a family of gradient regularization … to deal with adversarial examples. …

… Regularization is a fundamental and incisive method in optimization, its present zeitgeist …
machine learning. Through the introduction of a new component in the objective, regularization …

… The fundamental way to learn a generative model in machine learning is to (i) define a
parametric family of probability densities {Q✓}, ✓ 2 Θ ✓ Rd, and (ii) find parameters ✓⇤ 2 Θ such …

… The problem of adversarial examples has shown that … In this work, we study the degradation
through the regularization … NNs in a gentler way to avoid the problematic regularization. …

… a new regularization method based on virtual adversarial loss: … Virtual adversarial loss is
defined as the robustness of the … include simple and scalable machine learning algorithms. …

… and benefit of adversarial regularization based on adversarial … Additionally, we confirmed
that adversarial regularization … We believe that adversarial regularization can be one of the …

… Abstract—As we seek to deploy machine learning models beyond … adversarial (re)Training
as their main defense against perturbations. We also survey mothods that add regularization …