title: Reusing image-to-image GANs for spatial denoising of trajectories
body: <p>I work on particle tracking experiments that generate trajectories (x and y coordinates over time) from videos. Some experimental setups result in trajectories with a lot of spatial noise.</p>
  <p>I'm looking into using machine-learning models to denoise those trajectories, as our available algorithmic methods are limited. My goal is to train the model with two inputs : simulated trajectories as ground truth, and the same trajectories with induced noise.</p>
  <p>So far, most of the solutions I found regarding multiple inputs models that aren't classification or regression point to CNNs. However, I came across image-to-image denoising models (such as <a href="https://arxiv.org/abs/1611.07004" rel="nofollow noreferrer">https://arxiv.org/abs/1611.07004</a>) which seem to work based on the same relation between inputs, although with a different shape.</p>
  <p>Could it be feasible to readapt such a model for this purpose ?</p>