E-CapsGan: Generative adversarial network using capsule network as feature encoder

We explore using the theory of Capsule Network(CapsNet) in Generative Adversarial Network(GAN). The traditional Convolutional Neural Networks(CNNs) cannot explain the spatial relationship between the part and whole, so it will lose some of the target’s attribute information such as direction and posture. Capsule Network, proposed by Hinton in 2017, overcomes the defect of CNNs. In order to utilize the attributes of the target as much as possible, we propose the E-CapsGan which applies the CapsNet to encode the input image attribute features and guide the data generation of GAN. We explore the application of the E-CapsGan in two scenarios. For image generation, we propose the E-CapsGan1, which uses the CapsNet as an additional attribute feature encoder to obtain image attribute features to guide GAN. For image compression encoding, we explore the E-CapsGan2 which employs the CapsNet as the encoder to compress images into vectors, and GAN as the decoder to reconstruct the original images from vectors. On multiple datasets, qualitative and quantitative experiments are used to demonstrate the superior performance of E-CapsGan1 in image generation and the feasibility of E-CapsGan2 in image compression encoding.