when would u kindly publish your source code ? thx.
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Tweet je nedostupan.
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wow! that's a really innovative and interesting way to leverage adversarial perturbations
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Nice work! Yet another reason to get a better fundamental understanding of deep nets. What's next on the ever-increasing deep-net trickery? adversarial examples that trick the network into existing in a different reality?
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https://arxiv.org/abs/1805.07441 We use similar ideas to approach sequential learning problem. In your work, you want to generate adversarial program in the input space. Instead, We embed this structure in the parameters spaces alleviate the catastrophic forgetting.
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It is mentioned that the adversarial programs are "qualitatively" different for Inception and ResNet architectures, but there is no mention of corresponding quantitative effects.
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What happens when an adversarial program (towards MNIST) trained on Inception V3 is deployed on ResNet V2 152? Is the recognition performance at baseline or higher than the randomly-trained ResNet V2 152 case?
@gamaleldinfe
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