Best architecture for an autoencoder for 2D trajectory data? [D]

Hi,

I have a dataset that consists of 2D trajectories and I am aiming to develop an autoencoder architecture to learn a compressed set of features that reasonable represents and can reconstruct the trajectories.

The trajectories may look something like this as an example. A 2D image as input would seem to require a very sparse representation with high resolution to track the trajectory path. I am hoping there is a better way to input the path without requiring high …

2d image architecture autoencoder data dataset example features image learn look machinelearning set something trajectory