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Adversarial Examples Improve Image Recognition

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Nov 25 2019 Quoc Le

AdvProp: One weird trick to use adversarial examples to reduce overfitting. Key idea is to use two BatchNorms, one for normal examples and another one for adversarial examples. Significant gains on ImageNet and other test sets.
3 replies, 563 likes


Nov 25 2019 Mingxing Tan

Can adversarial examples improve image recognition? Check out our recent work: AdvProp, achieving ImageNet top-1 accuracy 85.5% (no extra data) with adversarial examples! Arxiv: https://arxiv.org/abs/1911.09665 Checkpoints: https://git.io/JeopW https://t.co/bAu054LGt2
2 replies, 372 likes


Nov 28 2019 Daisuke Okanohara

NN training with adversarial examples doesn't improve the generalization ability because of the distribution gap between clean and adversarial ones. Only using different BNs for clean and adversarial can solve this problem, achieving new SOTA ImageNet acc https://arxiv.org/abs/1911.09665
0 replies, 12 likes


Nov 24 2019 arXiv CS-CV

Adversarial Examples Improve Image Recognition http://arxiv.org/abs/1911.09665
0 replies, 4 likes


Nov 28 2019 akira

https://arxiv.org/abs/1911.09665 Significantly improved accuracy of ImageNet and ImageNet with noise using adversarial samples. By separating BN from adversarial one and normal data, changes in distribution due to data mixing are prevented. Simple but quite powerful. https://t.co/jGFNaUIkha
1 replies, 3 likes


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