Simple yet Effective Way for Improving the Performance of GAN

19 Nov 2019Yong-Goo ShinYoon-Jae YeoSung-Jea Ko

In adversarial learning, discriminator often fails to guide the generator successfully since it distinguishes between real and generated images using silly or non-robust features. To alleviate this problem, this brief presents a simple but effective way that improves the performance of generative adversarial network (GAN) without imposing the training overhead or modifying the network architectures of existing methods... (read more)

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