Generative adversarial networks (GANs) are highly effective unsupervised
learning frameworks that can generate very sharp data, even for data such as
images with complex, highly multimodal distributions. However GANs are known to
be very hard to train, suffering from problems such as mode collapse and
disturbing visual artifacts. Batch normalization (BN) techniques have been
introduced to address the training. Though BN accelerates the training in the
beginning, our experiments show that the use of BN can be unstable and
negatively impact the quality of the trained model. The evaluation of BN and
numerous other recent schemes for improving GAN training is hindered by the
lack of an effective objective quality measure for GAN models. To address these
issues, we first introduce a weight normalization (WN) approach for GAN
training that significantly improves the stability, efficiency and the quality
of the generated samples. To allow a methodical evaluation, we introduce
squared Euclidean reconstruction error on a test set as a new objective
measure, to assess training performance in terms of speed, stability, and
quality of generated samples. Our experiments with a standard DCGAN
architecture on commonly used datasets (CelebA, LSUN bedroom, and CIFAR-10)
indicate that training using WN is generally superior to BN for GANs, achieving
10% lower mean squared loss for reconstruction and significantly better
qualitative results than BN. We further demonstrate the stability of WN on a
21-layer ResNet trained with the CelebA data set. The code for this paper is
available at this https URL

本研究介绍了一种基于权重标准化的方法，用于改善生成对抗网络（GAN）的训练稳定性、效率和生成样本质量，并提供了基于测试集的平方欧几里得重构误差作为新的客观质量度量来评估训练性能，结果表明该方法优于基于批标准化（BN）的方法，并在常用数据集上取得了更好的定性和定量结果。