Generative adversarial networks with bio-inspired primary visual cortex for Industry 4.0

Abstract

Biologicalization (biological transformation) is an emerging trend in Industry 4.0 affecting digitization of manufacturing and related processes. It brings up the next generation of manufacturing technology and systems that extensively use biological and bio-inspired principles, materials, functions, structures and resources. This research is a contribution to the further convergence of computer and human vision for more robust and accurate automated object recognition and image generation. We present VOneGANs, a novel class of generative adversarial networks (GANs) with the qualitatively updated discriminative component. The new model incorporates a biologically constrained digital primary visual cortex V1. This earliest cortical visual area performs the first stage of human‘s visual processing and is believed to be a reason of its robustness and accuracy. Experiments with the updated architectures confirm the improved stability of GANs training and the higher quality of the automatically generated visual content. The promising results allow considering VOneGANs as providers of high-quality training content and as enablers of future simulation-based decision-making and decision-support tools for condition-monitoring, supervisory control, diagnostics, predictive maintenance, and cybersecurity in Industry 4.0.