Multi-Source Deep Domain Adaptation with Weak Supervision for Time-Series Sensor Data

Jana Doppa

Abstract:

Domain adaptation (DA) offers a valuable means to reuse data
and models for new problem domains. However, robust techniques
have not yet been considered for time series data with varying
amounts of data availability. In this paper, we make three main
contributions to fill this gap. First, we propose a novel Convolutional
deep Domain Adaptation model for Time Series data (CoDATS) that
significantly improves accuracy and training time over state-of-theart DA strategies on real-world sensor data benchmarks. By utilizing
data from multiple source domains, we increase the usefulness
of CoDATS to further improve accuracy over prior single-source
methods, particularly on complex time series datasets that have high
variability between domains. Second, we propose a novel Domain
Adaptation with Weak Supervision (DA-WS) method by utilizing
weak supervision in the form of target-domain label distributions,
which may be easier to collect than additional data labels. Third, we
perform comprehensive experiments on diverse real-world datasets
to evaluate the effectiveness of our domain adaptation and weak
supervision methods. Results show that CoDATS for single-source
DA significantly improves over the state-of-the-art methods, and
we achieve additional improvements in accuracy using data from
multiple source domains and weakly supervised signals