Handwritten Digit Image Generation Using Improved Generative Neural Network

Abstract

Generative adversarial network (GAN) is a new and effective neural network model. GANs are used in a lot of areas such as images and visual, speech and language, composing music, and creating 3D shapes. Although the GAN has a lot of applications, there are still some problems with it. Because not only the generator but also the discriminator needs to be trained, this model is easy to be unstable. And the GAN may not generate different images in human vision to make the generated images can safely pass the discriminator. Some methods will be shown to solve the problems of unstable and lack of diversity in this article by an example that generates handwritten digits. The methods are changing the optimizer from SGD to Adam or SGD with Adam, adding a Batchnorm, and combining GAN with CNN or RNN, each of the improvements will be contrasted with others by the chart of loss rate and the generated images. The research finds that each of the improvements has some advantages over the original GAN in the area of speed or stability. Especially, GAN with the optimizer Adam and DCGAN have the most stable consequence according to the image from the generator.