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.

翻译：在本文中,我们使用完全进化神经网络来对眼睛跟踪数据进行语义分解。 我们还利用这些网络进行重建,同时使用变式自动编码器来生成眼睛运动数据。我们方法的第一项改进是,由于使用了完全进化网络,因此任何输入大小都可以直接处理,因此不需要输入窗口。第二个改进是,使用和生成的数据是原始的眼跟踪数据(X、Y和时间),没有预处理。这是通过在第一层预先启动过滤器和在Z轴上建立输入振动器来实现的。我们评估了我们关于三个公开数据集的方法,并将结果与最新数据进行比较。