Training Artificial Immune Networks as Standalone Generative Models for Realistic Data Synthesis

In recent years, generative modelling has become a significant area of computer science research and artificial intelligence. This has been primarily due to the fact that generative models are useful in addressing the class imbalance problem inherent in some datasets. By generating synthetic data samples for underrepresented classes with a decent amount of variation through random noise, classification models could be trained more efficiently. The popularity of generative models was also increased by the prospect of being able to generate previously non-existent samples of images, audio and video for other creative tasks not related to addressing the class imbalance in datasets. This paper presents exploratory research to train an artificial immune network as a standalone generative model (called a generative adversarial artificial immune network, or GAAINet) using purely immunological computation concepts, such as antibody affinity, clonal selection and hypermutation. Experimental results show that the resulting generator artificial immune network could generate human-recognisable synthetic handwritten digits without any prior knowledge of the MNIST handwritten digits dataset.