Real-world sensing challenges such as sensor failures, communication issues, and power constraints lead to data intermittency. An issue that is known to undermine the traditional classification task that assumes a continuous data stream. Previous works addressed this issue by designing bespoke solutions (i.e. task-specific and/or modality-specific imputation). These approaches, while effective for their intended purposes, had limitations in their applicability across different tasks and sensor modalities. This raises an important question: Can we build a task-agnostic imputation pipeline that is transferable to new sensors without requiring additional training? In this work, we formalise the concept of zero-shot imputation and propose a novel approach that enables the adaptation of pre-trained models to handle data intermittency. This framework, named NeuralPrefix, is a generative neural component that precedes a task model during inference, filling in gaps caused by data intermittency. NeuralPrefix is built as a continuous dynamical system, where its internal state can be estimated at any point in time by solving an Ordinary Differential Equation (ODE). This approach allows for a more versatile and adaptable imputation method, overcoming the limitations of task-specific and modality-specific solutions. We conduct a comprehensive evaluation of NeuralPrefix on multiple sensory datasets, demonstrating its effectiveness across various domains. When tested on intermittent data with a high 50% missing data rate, NeuralPreifx accurately recovers all the missing samples, achieving SSIM score between 0.93-0.96. Zero-shot evaluations show that NeuralPrefix generalises well to unseen datasets, even when the measurements come from a different modality.

翻译：现实世界中的传感挑战（如传感器故障、通信问题和功率限制）会导致数据间歇性缺失。这一问题已知会破坏传统分类任务（其假设数据流是连续的）。先前的研究通过设计定制化解决方案（即任务特定和/或模态特定的插补方法）来解决此问题。这些方法虽然在其预期用途上有效，但在跨不同任务和传感器模态的适用性方面存在局限性。这引发了一个重要问题：能否构建一个任务无关的插补流程，使其能够迁移到新传感器而无需额外训练？在本工作中，我们形式化了零样本插补的概念，并提出了一种新颖的方法，使预训练模型能够适应处理数据间歇性问题。该框架名为NeuralPrefix，是一个生成式神经组件，在推理阶段置于任务模型之前，用于填补由数据间歇性造成的空缺。NeuralPrefix被构建为一个连续动力系统，其内部状态可通过求解常微分方程（ODE）在任何时间点进行估计。这种方法实现了一种更通用、适应性更强的插补方法，克服了任务特定和模态特定解决方案的局限性。我们在多个传感数据集上对NeuralPrefix进行了全面评估，证明了其在各领域的有效性。在缺失率高达50%的间歇性数据测试中，NeuralPrefix准确恢复了所有缺失样本，其结构相似性指数（SSIM）得分介于0.93至0.96之间。零样本评估表明，NeuralPrefix能够很好地泛化到未见过的数据集，即使测量数据来自不同的模态。