activity
20182022
most citedCALDA: Improving Multi-Source Time Series Domain Adaptation with Contrastive Adversarial Learning

7 citations · 10 across the 3 of their papers we have counts for

collaborators

5 papers

cs.LG2022★ 2 cited

Domain Adaptation Under Behavioral and Temporal Shifts for Natural Time Series Mobile Activity Recognition

Garrett Wilson, Janardhan Rao Doppa, Diane J. Cook

Increasingly, human behavior is captured on mobile devices, leading to an increased interest in automated human activity recognition. However, existing datasets typically consist o…

cs.LG2021★ 7 cited

CALDA: Improving Multi-Source Time Series Domain Adaptation with Contrastive Adversarial Learning

Garrett Wilson, Janardhan Rao Doppa, Diane J. Cook

Unsupervised domain adaptation (UDA) provides a strategy for improving machine learning performance in data-rich (target) domains where ground truth labels are inaccessible but can…

cs.LG2020

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

Garrett Wilson, Janardhan Rao Doppa, Diane J. Cook

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 v…

cs.LG2019★ 1 cited

Multi-Purposing Domain Adaptation Discriminators for Pseudo Labeling Confidence

Garrett Wilson, Diane J. Cook

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…

cs.LG2018

A Survey of Unsupervised Deep Domain Adaptation

Garrett Wilson, Diane J. Cook

Deep learning has produced state-of-the-art results for a variety of tasks. While such approaches for supervised learning have performed well, they assume that training and testing…