End-To-End Semi-supervised Learning for Differentiable Particle Filters

• URL: http://arxiv.org/abs/2011.05748v2
• Date: Sun, 28 Mar 2021 14:15:04 GMT
• Title: End-To-End Semi-supervised Learning for Differentiable Particle Filters
• Authors: Hao Wen, Xiongjie Chen, Georgios Papagiannis, Conghui Hu and Yunpeng Li
• Abstract summary: Recent advances in incorporating neural networks into particle filters provide the desired flexibility to apply particle filters in real-world applications. Past efforts in optimising such models often require the knowledge of true states which can be expensive to obtain or even unavailable in practice. We present an end-to-end learning objective based upon the maximisation of a pseudo-likelihood function which can improve the estimation of states when large portion of true states are unknown.
• Score: 15.187145925738553
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent advances in incorporating neural networks into particle filters provide the desired flexibility to apply particle filters in large-scale real-world applications. The dynamic and measurement models in this framework are learnable through the differentiable implementation of particle filters. Past efforts in optimising such models often require the knowledge of true states which can be expensive to obtain or even unavailable in practice. In this paper, in order to reduce the demand for annotated data, we present an end-to-end learning objective based upon the maximisation of a pseudo-likelihood function which can improve the estimation of states when large portion of true states are unknown. We assess performance of the proposed method in state estimation tasks in robotics with simulated and real-world datasets.

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