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Found 5 papers, 3 papers with code
Domain Adaptation Under Behavioral and Temporal Shifts for Natural Time Series Mobile Activity Recognition
Because of the large variations present in human behavior, we collect data from many participants across two different age groups.
CALDA: Improving Multi-Source Time Series Domain Adaptation with Contrastive Adversarial Learning
CALDA synergistically combines the principles of contrastive learning and adversarial learning to robustly support multi-source UDA (MS-UDA) for time series data.
Multi-Source Deep Domain Adaptation with Weak Supervision for Time-Series Sensor Data
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.
Multi-Purposing Domain Adaptation Discriminators for Pseudo Labeling Confidence
Often domain adaptation is performed using a discriminator (domain classifier) to learn domain-invariant feature representations so that a classifier trained on labeled source data will generalize well to unlabeled target data.
A Survey of Unsupervised Deep Domain Adaptation
Deep learning has produced state-of-the-art results for a variety of tasks.
