29,254 research outputs found
Generative Adversarial Trainer: Defense to Adversarial Perturbations with GAN
We propose a novel technique to make neural network robust to adversarial
examples using a generative adversarial network. We alternately train both
classifier and generator networks. The generator network generates an
adversarial perturbation that can easily fool the classifier network by using a
gradient of each image. Simultaneously, the classifier network is trained to
classify correctly both original and adversarial images generated by the
generator. These procedures help the classifier network to become more robust
to adversarial perturbations. Furthermore, our adversarial training framework
efficiently reduces overfitting and outperforms other regularization methods
such as Dropout. We applied our method to supervised learning for CIFAR
datasets, and experimantal results show that our method significantly lowers
the generalization error of the network. To the best of our knowledge, this is
the first method which uses GAN to improve supervised learning
Robust Conditional GAN from Uncertainty-Aware Pairwise Comparisons
Conditional generative adversarial networks have shown exceptional generation
performance over the past few years. However, they require large numbers of
annotations. To address this problem, we propose a novel generative adversarial
network utilizing weak supervision in the form of pairwise comparisons (PC-GAN)
for image attribute editing. In the light of Bayesian uncertainty estimation
and noise-tolerant adversarial training, PC-GAN can estimate attribute rating
efficiently and demonstrate robust performance in noise resistance. Through
extensive experiments, we show both qualitatively and quantitatively that
PC-GAN performs comparably with fully-supervised methods and outperforms
unsupervised baselines.Comment: Accepted for spotlight at AAAI-2
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