Learning With Adversary

By Ruitong Huang, Bing Xu, Dale Schuurmans, Csaba Szepesvari. 2015

15 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Alexander Ororbia
2. C. Lee Giles
3. Aysegul Dundar
4. Eugenio Culurciello
5. Jonghoon Jin
6. Pedro Tabacof
7. Eduardo Valle
8. Daniel Kifer

Citations in the corpus

• Robust Convolutional Neural Networks under Adversarial Noise (2015, background): “While Bishop (1995) showed that training with noise is equivalent to a regularizer, Goodfellow et al. (2014); Huang et al. (2015) used adversarial perturbation during training but not has been applied to natural images or more challenging image classification tasks.”
• Unifying Adversarial Training Algorithms with Flexible Deep Data Gradient Regularization (2016, background): “In particular, it helps explain some of the newer approaches to adversarial training (Miyato et al., 2015; Huang et al., 2015).”
• Exploring the space of adversarial images (2015, background): “Recent work shows that it is possible for the training procedure to make classifiers robust to adversarial examples by using a strong adversary [8] or defensive distillation [9].”