Self-supervised contrastive pre-training for time series via time-frequency consistency
Pre-training on time series poses a unique challenge due to the potential mismatch between
pre-training and target domains, such as shifts in temporal dynamics, fast-evolving trends …
pre-training and target domains, such as shifts in temporal dynamics, fast-evolving trends …
Model adaptation: Historical contrastive learning for unsupervised domain adaptation without source data
Unsupervised domain adaptation aims to align a labeled source domain and an unlabeled
target domain, but it requires to access the source data which often raises concerns in data …
target domain, but it requires to access the source data which often raises concerns in data …
Adversarial unsupervised domain adaptation for hand gesture recognition using thermal images
Hand gesture recognition has a wide range of applications, including in the automotive and
industrial sectors, health assistive systems, authentication, and so on. Thermal images are …
industrial sectors, health assistive systems, authentication, and so on. Thermal images are …
Trustworthy representation learning across domains
As AI systems have obtained significant performance to be deployed widely in our daily live
and human society, people both enjoy the benefits brought by these technologies and suffer …
and human society, people both enjoy the benefits brought by these technologies and suffer …
An unsupervised domain adaptation method towards multi-level features and decision boundaries for cross-scene hyperspectral image classification
C Zhao, B Qin, S Feng, W Zhu… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Despite success in the same-scene hyperspectral image classification (HSIC), for the cross-
scene classification, samples between source and target scenes are not drawn from the …
scene classification, samples between source and target scenes are not drawn from the …
Coco: A coupled contrastive framework for unsupervised domain adaptive graph classification
Although graph neural networks (GNNs) have achieved impressive achievements in graph
classification, they often need abundant task-specific labels, which could be extensively …
classification, they often need abundant task-specific labels, which could be extensively …
A Survey of Trustworthy Representation Learning Across Domains
As AI systems have obtained significant performance to be deployed widely in our daily live
and human society, people both enjoy the benefits brought by these technologies and suffer …
and human society, people both enjoy the benefits brought by these technologies and suffer …
Deal: An unsupervised domain adaptive framework for graph-level classification
Graph neural networks (GNNs) have achieved state-of-the-art results on graph classification
tasks. They have been primarily studied in cases of supervised end-to-end training, which …
tasks. They have been primarily studied in cases of supervised end-to-end training, which …
Dual-bridging with adversarial noise generation for domain adaptive rppg estimation
The remote photoplethysmography (rPPG) technique can estimate pulse-related metrics (eg
heart rate and respiratory rate) from facial videos and has a high potential for health …
heart rate and respiratory rate) from facial videos and has a high potential for health …
Make the u in uda matter: Invariant consistency learning for unsupervised domain adaptation
Abstract Domain Adaptation (DA) is always challenged by the spurious correlation between
the domain-invariant features (eg, class identity) and the domain-specific ones (eg …
the domain-invariant features (eg, class identity) and the domain-specific ones (eg …