Harnessing the power of GANs and meta learning for few-shot image generation

Abstract

This chapter tackles the problem of few-shot image generation by investigating the combination of meta-learning and Generative Adversarial Networks (GANs). As the need for effective and high-quality image synthesis increases conventional techniques frequently find it difficult to produce a wide range of realistic images with a small number of training examples. We present a framework that combines the advantages of GANs-which are excellent at generating high-fidelity images-with meta-learning strategies which help models efficiently learn from a small number of examples. Through the combination of these two methods, we put forth new tactics that improve GANs flexibility and few-shot performance. In order to enhance performance on tasks requiring quick learning from little data we go over the fundamental ideas of both meta-learning and GAN architectures. We validate the effectiveness of our method with extensive experiments and case studies showing gains in image diversity quality and generation speed over traditional methods.

Keywords Photointerpretation, Adversarial networks, Conventional techniques, Networks learning, Performance, Power, Realistic images, Generative adversarial networks