Evolutionary Algorithm based Encoder Decoder Network Design for Semantic Inpainting and Noise Vector Mapping in Generative Adversarial Network

The work presented in this paper focuses on the use of Evolutionary Algorithms (EA) in two different fields of interest where researchers are keenly working on - Encoder Decoder and Generative Adversarial Network architectures. These architectures have brought in wonders to the field of Image processing. They gain interests of researches in various other fields like image domain mappings, interactive generation, security, detection of patterns and processing etc. The key aspects that determine a good performance of a system are its model architecture and optimal hyper parameters that drives it. This paper proposes two new analysis with Evolutionary Algorithms 1) Hyper parameter optimization of Encoder Decoder Model for the task of Image Inpainting and 2) Addressing the input noise vector of the Generative Adversarial Network model. In the first analysis, the research optimizes the task of semantic inpainting. The aim of inpainting is to do the semantic filling of the missing regions in original image. Inpainting task is used across various domains like medical imaging to reconstruct the damaged body parts and tissues, satellite imaging and surveillance to have semantic filling of images taken in uncertain weather conditions and in photography which allows the range of fillings possible to better fulfill the customer needs etc. The research found a set of hyper parameters which can perform similar to/outperform the manually hard coded parameters. It is really difficult task for a person to try out all the combinations and evolutionary algorithms tend to do it very easily. In second analysis, the research focuses on the input noise vector to the generator of Generative Adversarial Network. After training the generator is capable of mapping the random noise vector to an image. This study addresses grouping all those vectors together which are capable of mapping to a particular class of generation using evolutionary algorithms. This way the present study addressed the phenomenon of mapping of Generative Adversarial Networks. For both the above analysis CIFAR10 Dataset is used.