Publications › CIKM 2025, Seoul, South Korea
High-Order Moments Conditional Domain Adaptation Networks for Wearable Human Activity Recognition
Abstract
Developing scalable wearable human activity recognition (wHAR) models is challenging due to domain shifts that substantially degrade performance across downstream tasks. Unsupervised domain adaptation (UDA) seeks to improve generalization by transferring knowledge from labeled source domains to unlabeled target domains. However, conventional UDA methods primarily align marginal feature distributions while neglecting feature-label dependencies, often leading to negative transfer and sub-optimal performance. Motivated by these limitations, we propose a novel optimization framework that tackles two key challenges: (i) generating reliable pseudo-labels for the unlabeled target domain and (ii) minimizing conditional discrepancies across domains. To address (i), we employ temperature-based entropy minimization (TEM), which calibrates prediction confidence by scaling logits with a temperature parameter to produce robust pseudo-labels. For (ii), we introduce a polynomial kernel-based cross-covariance (PkCC) loss, a high-order statistics-driven approach that maps features into a reproducing kernel hilbert space (RKHS) to capture richer feature-label dependencies and reduce conditional distribution gaps between domains. In addition, we demonstrate that CoDAN readily extends to partial UDA (pUDA), where the target label space is a subset of the source, and extensive evaluations on public wHAR datasets with diverse label spaces validate its superior performance over state-of-the-art methods in both UDA and pUDA scenarios.
BibTeX
@inproceedings{ghosh2025highorder,
title={High-Order Moments Conditional Domain Adaptation Networks for Wearable Human Activity Recognition},
author={Ghosh, I. and Chugh, G. and Faridee, A.Z.M. and Roy, N.},
booktitle={CIKM 2025, Seoul, South Korea},
year={2025},
url={https://dl.acm.org/doi/10.1145/3746252.3761225}
}