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To be clear I meant this paper (http://arxiv.org/abs/1312.6199 http://arxiv.org/abs/1312.6199) as the original paper for adversarial images. I think they did tr
by Houshalter 12y ago
To be clear I meant this paper (http://arxiv.org/abs/1312.6199 http://arxiv.org/abs/1312.6199) as the original paper for adversarial images. I think they did try transferring them between very different NNs:
>In addition, the specific nature of these perturbations is not a random artifact of learning: the same perturbation can cause a different network, that was trained on a different subset of the dataset, to misclassify the same input.
- compbio 12y agoVery interesting. Thank you for correcting me. a relatively large fraction of examples will be misclassified by networks trained from scratch with different hyper-parameters (number of layers, regularization or initial weights). The above observations suggest that adversarial examples are somewhat universal...