ROBUST TIME SERIES RECOVERY AND CLASSIFICATION USING TEST-TIME NOISE SIMULATOR NETWORKS.
ROBUST TIME SERIES RECOVERY AND CLASSIFICATION USING TEST-TIME NOISE SIMULATOR NETWORKS.
复制标题
DOI：
10.1109/icassp49357.2023.10096888
复制
发表时间：
2023-06
期刊：
影响因子：
--
通讯作者：
Turaga, Pavan
中科院分区：
文献类型：
--
作者：
Jeon, Eun Som;Lohit, Suhas;Anirudh, Rushil;Turaga, Pavan
由于传感器级别的变化和缺陷，时间序列通常容易受到各种类型的损坏，这些损坏可能导致丢失样本、传感器和量化噪声、未知校准、未知相移等。这些损坏无法轻松校正，因为部署时噪声模型可能未知。这也导致无法使用预先训练的分类器，这些分类器是在（干净的）源数据上训练的。在本文中，我们提出了一个一般的框架和模型的时间序列，可以利用（未标记的）测试样本来估计噪声模型，完全在测试时间。为此，我们使用一个耦合的解码器模型和一个额外的神经网络，它作为一个学习的噪声模型模拟器。我们表明，该框架能够“清理”数据，以便匹配源训练数据统计数据，并且清理后的数据可以直接与预训练的分类器一起使用，以进行稳健的预测。我们在不同的应用领域与不同类型的传感器进行实证研究，清楚地证明了这种方法的有效性和通用性。
Time-series are commonly susceptible to various types of corruption due to sensor-level changes and defects which can result in missing samples, sensor and quantization noise, unknown calibration, unknown phase shifts etc. These corruptions cannot be easily corrected as the noise model may be unknown at the time of deployment. This also results in the inability to employ pre-trained classifiers, trained on (clean) source data. In this paper, we present a general framework and models for time-series that can make use of (unlabeled) test samples to estimate the noise model—entirely at test time. To this end, we use a coupled decoder model and an additional neural network which acts as a learned noise model simulator. We show that the framework is able to “clean” the data so as to match the source training data statistics and the cleaned data can be directly used with a pre-trained classifier for robust predictions. We perform empirical studies on diverse application domains with different types of sensors, clearly demonstrating the effectiveness and generality of this method.