A Novel GAN based on Progressive Growing Transformer with Capsule Embedding

Generative Adversarial Networks (GANs) have achieved great improvement after using Convolutional Neural Networks (CNNs) instead of Multi-Layer Perceptrons (MLPs) to build network architecture. Recently, since Transformer architecture has performed well in compute vision, building a Transformer-based image generation network helps solve some of problems caused by CNNs e.g. CNNs-based GANs are difficult to train. On the other hand, the learning of positional encoding in the Transformer structure is often ignored in Transformer-based GANs. Capsule networks are usually considered to be able to learn position information in image features. Therefore, this paper constructs a Progressive Growing Transformer network with Capsule Embedding GAN (PGTCEGAN). The results from the proposed approach are promising with 3.59 FID and 3.92 FID on CelebA and LSUN-Church datasets respectively in image generation task.