# Cycle Generative Adversarial Network Based on Gradient Normalization for Infrared Image Generation
## 1 Introduction
In the discriminator, the authors utilized three discriminators of the same structure responsible for the discriminative task at different scales and increased the image resolution at a deeper level to obtain high-definition images. However, the training process of the pix2 pixHD algorithm was not stable enough. [PERSON] et al. presented the Wasserstein generative adversarial network (WGAN) in 2018 [12; 13]. The WGAN method restricts the parameters in the discriminator by cutting the weights of the discriminator to render the discriminator less discriminative, and thus completely solved the instability problem of the generative adversarial network model. In 2018, [PERSON] et al. put forward the self-attentive generative adversarial network (SAGAN) [14; 15]. The SAGAN method addressed the limitation of convolutional local perceptual field. A self-attentive mechanism was introduced in the discriminator and generator, and global feature information was obtained in this way. Although this method achieved better results with fewer iterations, it was highly unstable and prone to crashing during model training. In the field of unsupervised learning for image generation, [PERSON] et al. 2016 proposed coupled generative adversarial networks (COGANs) for unpaired datasets [16]. This approach set out to learn the joint distribution of multi-domain images by partial weight sharing. The generators share the weights of the first half, and the discriminatorsextract the high-level features of the second half. [PERSON] et al. 2016 presented the domain transfer network (DTN) [17; 18; 19]. The DTN network employs a compound loss function to solve the general analogy problem by separating unlabeled samples in the given domain via a multivariate function to learn new mapping relationships for image conversion, while the domain asymmetry and the small amount of information contained in the new source domain leads to unsatisfactory results. In 2017, [PERSON] et al. put forward a cycle generative adversarial network [20], which used a dual generator with a dual discriminator to implement bidirectional conversion of source-domain images to target-domain images. [PERSON] et al. concluded that if it was possible to generate target-domain images from source-domain images, then generating source-domain images from target-domain images is also feasible; however, this model does not easily converge during training and the quality of the generated images is usually not high. In 2018, [PERSON] et al. proposed StarGAN [21; 22]. StarGAN implements transformation between multiple domain images and has the advantage that for x-field transformation, CycleGAN needs to learn x*(x - 1) models, while StarGAN only needs to learn one model. The disadvantage of StarGAN is a large sample size requirement. [PERSON] et al. proposed the Munit algorithm in 2018 [23]. The Munit algorithm uses both shared content space and differentiated style space for image generation. When generating an image, the same content and different styles were combined and encoded for output, thus allowing multimodal image generation and multimodal image conversion. In 2019, [PERSON] et al. presented the FUNIT algorithm [24; 25], which mainly solved the image conversion problem for small sample images and untouched regions. Its network framework is composed of a conditional image generation network and a multitask adversarial discriminative network, which achieves image generation by computing a small number of samples. The method bears strong generalization capability. Despite many researchers having continuously improve and optimize the algorithms to make the generated images consistent with the target images, the problems of model instability, easy collapse and low quality of image generation have not been sufficiently recognized.