Title: On the Effects of Batch and Weight Normalization in Generative Adversarial Networks,
Abstract: 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