Faster Projected GAN: Towards Faster Few-Shot Image Generation
Chuang Wang Zhengping Li Yuwen Hao Lijun Wang Xiaoxue Li
In order to solve the problems of long training time, large consumption of
computing resources and huge parameter amount of GAN network in image
generation, this paper proposes an improved GAN network model, which is named
Faster Projected GAN, based on Projected GAN. The proposed network is mainly
focuses on the improvement of generator of Projected GAN. By introducing depth
separable convolution (DSC), the number of parameters of the Projected GAN is
reduced, the training speed is accelerated, and memory is saved. Experimental
results show that on ffhq-1k, art-painting, Landscape and other few-shot image
datasets, a 20% speed increase and a 15% memory saving are achieved. At the
same time, FID loss is less or no loss, and the amount of model parameters is
better controlled. At the same time, significant training speed improvement has
been achieved in the small sample image generation task of special scenes such
as earthquake scenes with few public datasets.