Unsupervised domain adaptation for regression using dictionary learning
Unsupervised domain adaptation aims to generalize the knowledge learned on a labeled
source domain across an unlabeled target domain. Most of existing unsupervised …
source domain across an unlabeled target domain. Most of existing unsupervised …
Dual adversarial network with meta-learning for domain-generalized few-shot text classification
Meta-learning-based methods prevail in few-shot text classification. Current methods
perform meta-training and meta-testing on two parts of a dataset in the same or similar …
perform meta-training and meta-testing on two parts of a dataset in the same or similar …
mmWave Wi-Fi trajectory estimation with continuous-time neural dynamic learning
We leverage standards-compliant beam training measurements from commercial-of-the-
shelf (COTS) 802.11 ad/ay devices for localization of a moving object. Two technical …
shelf (COTS) 802.11 ad/ay devices for localization of a moving object. Two technical …
Object Trajectory Estimation with Continuous-Time Neural Dynamic Learning of Millimeter-Wave Wi-Fi
In this paper, we leverage standard-compliant beam training measurements from
commercial millimeter-wave (mmWave) Wi-Fi communication devices for object localization …
commercial millimeter-wave (mmWave) Wi-Fi communication devices for object localization …
Embedded Representation Learning Network for Animating Styled Video Portrait
The talking head generation recently attracted considerable attention due to its widespread
application prospects, especially for digital avatars and 3D animation design. Inspired by …
application prospects, especially for digital avatars and 3D animation design. Inspired by …
Leveraging unsupervised data and domain adaptation for deep regression in low-cost sensor calibration
Air quality monitoring is becoming an essential task with rising awareness about air quality.
Low-cost air quality sensors are easy to deploy but are not as reliable as the costly and …
Low-cost air quality sensors are easy to deploy but are not as reliable as the costly and …
Domain Generalization in Regression
In the context of classification,\textit {domain generalization}(DG) aims to predict the labels of
unseen target-domain data only using labeled source-domain data, where the source and …
unseen target-domain data only using labeled source-domain data, where the source and …