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.

翻译：可微分粒子滤波器将神经网络的灵活性与序贯蒙特卡洛方法的概率性质相结合。然而,传统方法依赖于有标签数据(即潜在状态的真实信息)的可用性,这在现实应用中往往难以获取。本文比较了两种半监督训练目标对可微分粒子滤波器的有效性。我们在两个有标签数据稀缺的模拟环境中展示了实验结果。