Adversarial training reduces loss sharpness and improves cross-Monte-Carlo generalization for Higgs-jet classifiers, with projected gradient descent giving the largest gains.
Therefore, a tradi- tional dense model is chosen and consists of three hidden layers of sizes 128, 64, and 32, respectively
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Enhancing generalization in high energy physics using white-box adversarial attacks
Adversarial training reduces loss sharpness and improves cross-Monte-Carlo generalization for Higgs-jet classifiers, with projected gradient descent giving the largest gains.