We introduce Variational State-Space Filters (VSSF), a new method for unsupervised learning, identification, and filtering of latent Markov state space models from raw pixels. We present a theoretically sound framework for latent state space inference under heterogeneous sensor configurations. The resulting model can integrate an arbitrary subset of the sensor measurements used during training, enabling the learning of semi-supervised state representations, thus enforcing that certain components of the learned latent state space to agree with interpretable measurements. From this framework we derive L-VSSF, an explicit instantiation of this model with linear latent dynamics and Gaussian distribution parameterizations. We experimentally demonstrate L-VSSF's ability to filter in latent space beyond the sequence length of the training dataset across several different test environments.
翻译:我们引入了变化式国家空间过滤器(VSSF),这是从原始像素中不经监督地学习、识别和过滤潜伏马尔科夫状态空间模型的新方法。我们为不同传感器配置下的潜伏状态空间推断提供了一个在理论上健全的框架。由此形成的模型可以将培训期间使用的传感器测量的任意子集整合在一起,从而能够学习半受监督状态表示法,从而强制要求已学过潜伏状态空间的某些组成部分与可解释的测量法一致。我们从这个框架中得出L-VSSF,这一模型与线性潜伏动态和高斯分布参数的明显同步化。我们实验性地展示了L-VSF在各种不同的测试环境中超越培训数据集的序列长度在潜伏空间中过滤的能力。