Multi-Source Deep Domain Adaptation with Weak Supervision for Time-Series Sensor Data (2005.10996v1)

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-the-art 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. Code is available at: https://github.com/floft/codats

• The paper introduces CoDATS, a CNN-based model that outperforms RNNs in transferring knowledge for time-series sensor data.
• It leverages multi-source domain adaptation to enhance performance across varied and imbalanced datasets.
• Integrating weak supervision via KL divergence, the approach achieves faster training times and improved target accuracy.

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

The paper presents an innovative approach to domain adaptation (DA) with a focus on improving the transfer of knowledge across domains specifically in the context of time-series sensor data. This research addresses critical challenges in the application of DA methods to time series, leveraging CNN architectures rather than the more traditional RNNs to enhance both computational efficiency and accuracy.

Core Contributions

1. Convolutional Network for DA in Time Series: The authors propose a novel framework called CoDATS (Convolutional deep Domain Adaptation model for Time Series), specifically designed to handle time-series data. CoDATS utilizes CNN architectures to improve model training times significantly and yields superior accuracy as compared to the state-of-the-art DA methods like VRADA and R-DANN which employ RNNs. This shift from RNNs to CNNs aligns with the empirical findings that CNNs often surpass RNNs on sequential tasks due to their capability in capturing temporal dependencies efficiently.
2. Multi-Source Domain Adaptation Capabilities: CoDATS is adaptable for use in both single-source and multi-source DA contexts, where data can be drawn from multiple source domains. Such flexibility is crucial when domains exhibit high variability, and comprehensive empirical evaluations presented in the paper demonstrate that leveraging multiple sources notably boosts performance.
3. Domain Adaptation with Weak Supervision (DA-WS): A novel methodological addition involves combining weak supervision signals with DA, wherein the target domain label distributions are integrated as a form of soft constraints during model training. This weak supervision is leveraged using the KL divergence, ensuring that the predicted label distribution aligns with known target label proportions. DA-WS further enhances standard domain adaptation, particularly beneficial in scenarios with imbalanced class distributions across domains.

Experimental Insights

The evaluation spans a range of datasets, including HAR, HHAR, WISDM, and uWave, covering various time-series classifications like human activity and gesture recognition. CoDATS significantly outperforms RNN-based DA methods, maintaining lower training times while achieving higher target domain accuracy. These results are persistent across different domains and configurations, suggesting a robust methodology for practical deployment in environments where labeling is costly or difficult.

Moreover, integrating weak supervision into the DA pipeline (CoDATS-WS) yields additional accuracy gains, especially in datasets with skewed class distributions. While CoDATS already demonstrates improved performance on balanced datasets, CoDATS-WS is particularly advantageous for imbalanced datasets like WISDM, pointing towards new possibilities in DA where additional but often more accessible label-related information can be utilized.

Implications and Future Directions

The implications of this work are significant for the domain of time-series analysis and real-world applications requiring domain adaptation. The shift towards CNNs suggests a new trajectory for time-series data treatment, advocating its efficiency in scenarios previously dominated by RNNs. Practically, the integration of multi-source data expands the robustness and generalization capacity of predictive models derived from diverse sensor setups and participant variations.

Theoretical elements proposed like DA-WS highlight the importance of incorporating auxiliary domain information, potentially opening avenues for further explorations into other non-traditional forms of supervision or constraints in DA.

Looking forward, extensions might include developing frameworks for heterogeneous feature sets or incorporating DA techniques into more advanced architectures like those proposed in the InceptionTime model for time-series data, facilitating an even broader adoption across industries reliant on sensor-generated insights. Additionally, exploration into the dynamics of negative transfer in multi-source setups could yield refinements to ensure consistently positive transfer outcomes.