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Is the next step generating adversarial examples and injecting them into the training pipeline?

• blt 9y ago
  I think there have been some papers on that already. Sorry, I don't know them off the top of my head. It's definitely a good idea.
  • moyix 9y ago
    Yes: > Adversarial training seeks to improve the generalization of a model when presented with adversarial examples at test time by proactively generating adversarial examples as part of the training procedure. This idea was first introduced by Szegedy et al. [SZS13] but was not yet practical because of the high computation cost of generating adversarial examples. Goodfellow et al. showed how to generate adversarial examples inexpensively with the fast gradient sign method and made it computationally efficient to generate large batches of adversarial examples during the training process [GSS14]. The model is then trained to assign the same label to the adversarial example as to the original example—for example, we might take a picture of a cat, and adversarially perturb it to fool the model into thinking it is a vulture, then tell the model it should learn that this picture is still a cat. An open-source implementation of adversarial training is available in the cleverhans library and its use illustrated in the following tutorial. http://www.cleverhans.io/security/privacy/ml/2017/02/15/why-attacking-machine-learning-is-easier-than-defending-it.html http://www.cleverhans.io/security/privacy/ml/2017/02/15/why-... https://arxiv.org/abs/1312.6199 https://arxiv.org/abs/1312.6199 https://arxiv.org/abs/1412.6572 https://arxiv.org/abs/1412.6572