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cs.LG2021★ 1 cited
Towards Speeding up Adversarial Training in Latent Spaces
Yaguan Qian, Qiqi Shao, Tengteng Yao +5
Adversarial training is wildly considered as one of the most effective way to defend against adversarial examples. However, existing adversarial training methods consume unbearable…
cs.LG2020★ 1 cited
Noise Homogenization via Multi-Channel Wavelet Filtering for High-Fidelity Sample Generation in GANs
Shaoning Zeng, Bob Zhang
In the generator of typical Generative Adversarial Networks (GANs), a noise is inputted to generate fake samples via a series of convolutional operations. However, current noise ge…