This paper introduces a novel weight normalization technique called spectral normalization to stabilize the training of discriminators in generative adversarial networks (GANs). Spectral normalization controls the Lipschitz constant of the discriminator by normalizing the spectral norm of each layer's weight matrix, ensuring that the largest singular value of the weight matrix is 1. This method is computationally efficient and easy to implement, requiring only the tuning of a single hyper-parameter. The authors compare spectral normalization with other regularization techniques such as weight normalization, weight clipping, and gradient penalty, demonstrating that it consistently produces better or comparable results in terms of image quality and stability. Experiments on datasets like CIFAR10, STL-10, and ILSVRC2012 show that spectrally normalized GANs (SN-GANs) generate more diverse and high-quality images compared to conventional methods. The paper also discusses the theoretical foundations of spectral normalization, its implementation details, and its effectiveness in improving the performance of GANs.This paper introduces a novel weight normalization technique called spectral normalization to stabilize the training of discriminators in generative adversarial networks (GANs). Spectral normalization controls the Lipschitz constant of the discriminator by normalizing the spectral norm of each layer's weight matrix, ensuring that the largest singular value of the weight matrix is 1. This method is computationally efficient and easy to implement, requiring only the tuning of a single hyper-parameter. The authors compare spectral normalization with other regularization techniques such as weight normalization, weight clipping, and gradient penalty, demonstrating that it consistently produces better or comparable results in terms of image quality and stability. Experiments on datasets like CIFAR10, STL-10, and ILSVRC2012 show that spectrally normalized GANs (SN-GANs) generate more diverse and high-quality images compared to conventional methods. The paper also discusses the theoretical foundations of spectral normalization, its implementation details, and its effectiveness in improving the performance of GANs.