  In this paper, we use fully convolutional neural networks for the semantic
segmentation of eye tracking data. We also use these networks for
reconstruction, and in conjunction with a variational auto-encoder to generate
eye movement data. The first improvement of our approach is that no input
window is necessary, due to the use of fully convolutional networks and
therefore any input size can be processed directly. The second improvement is
that the used and generated data is raw eye tracking data (position X, Y and
time) without preprocessing. This is achieved by pre-initializing the filters
in the first layer and by building the input tensor along the z axis. We
evaluated our approach on three publicly available datasets and compare the
results to the state of the art.
