  The visual scanpath is a sequence of points through which the human gaze
moves while exploring a scene. It represents the fundamental concepts upon
which visual attention research is based. As a result, the ability to predict
them has emerged as an important task in recent years. In this paper, we
propose an inter-observer consistent adversarial training approach for scanpath
prediction through a lightweight deep neural network. The adversarial method
employs a discriminative neural network as a dynamic loss that is better suited
to model the natural stochastic phenomenon while maintaining consistency
between the distributions related to the subjective nature of scanpaths
traversed by different observers. Through extensive testing, we show the
competitiveness of our approach in regard to state-of-the-art methods.
