W2GAN: Importance Weight and Wavelet feature guided Image-to-Image translation under limited data

Qiuxia Yang, Yuanyuan Pu, Zhengpeng Zhao, Dan Xu, Siqi Li

Abstract: Highlights•In this paper, the GANs are trained under limited data. This paper overcomes the problem of the discriminator over-fitting during the training, which leads to the model divergence and the results degradation, stabilizing the training process and achieving higher quality results. From the qualitative and quantitative perspectives, this paper has achieved competitive results in current research.•This paper introduces the learnable importance weight to the adversarial loss, which aims to hope the high-quality images produce higher influence during the training generator. It relieves the problems of the training diverge and over-fitting.•This paper proposes a Wavelet-AdaIN Normalization to learn the high-frequency features, which adaptively integrates high-frequency statistical characteristics from generated features and real image high-frequency information. It encourages the generator to produce precise high-frequency signals with fine details.