LAUSR

Since generative adversarial network (GAN) can learn data distribution and generate new samples based on the learned data distribution, it has become a research hotspot in the area of deep learning and cognitive computation. The learning of GAN heavily depends on a large set of training data. However, in many real-world applications, it is difficult to acquire a large number of data as needed.  In this paper, we propose a novel generative adversarial network called ML-CGAN for generating authentic and diverse images with few training data. Particularly, ML-CGAN consists of two modules: the conditional generative adversarial network (CGAN) backbone and the meta-learner structure. The CGAN backbone is applied to generate images, while the meta-learner structure is an auxiliary network to provide deconvolutional weights for the generator of the CGAN backbone.  Qualitative and quantitative experimental results on the MNIST, Fashion MNIST, CelebA and CIFAR-10 data sets demonstrate the superiority of ML-CGAN over state-of-the-art models. Specifically, the results show that the meta-learner structure can learn prior knowledge and transfer it to the new tasks, which is beneficial for generating authentic and diverse images in the new tasks with few training data.