  Differentiable particle filters combine the flexibility of neural networks
with the probabilistic nature of sequential Monte Carlo methods. However,
traditional approaches rely on the availability of labelled data, i.e., the
ground truth latent state information, which is often difficult to obtain in
real-world applications. This paper compares the effectiveness of two
semi-supervised training objectives for differentiable particle filters. We
present results in two simulated environments where labelled data are scarce.
