Transfer learning in environmental remote sensing
Abstract Machine learning (ML) has proven to be a powerful tool for utilizing the rapidly
increasing amounts of remote sensing data for environmental monitoring. Yet ML models …
increasing amounts of remote sensing data for environmental monitoring. Yet ML models …
Can emotion be transferred?—A review on transfer learning for EEG-based emotion recognition
W Li, W Huan, B Hou, Y Tian, Z Zhang… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
The issue of electroencephalogram (EEG)-based emotion recognition has great academic
and practical significance. Currently, there are numerous research trying to address this …
and practical significance. Currently, there are numerous research trying to address this …
Domain adaptive ensemble learning
The problem of generalizing deep neural networks from multiple source domains to a target
one is studied under two settings: When unlabeled target data is available, it is a multi …
one is studied under two settings: When unlabeled target data is available, it is a multi …
Optimal transport for treatment effect estimation
Estimating individual treatment effects from observational data is challenging due to
treatment selection bias. Prevalent methods mainly mitigate this issue by aligning different …
treatment selection bias. Prevalent methods mainly mitigate this issue by aligning different …
DeepCDA: deep cross-domain compound–protein affinity prediction through LSTM and convolutional neural networks
Motivation An essential part of drug discovery is the accurate prediction of the binding affinity
of new compound–protein pairs. Most of the standard computational methods assume that …
of new compound–protein pairs. Most of the standard computational methods assume that …
Deep learning in drug target interaction prediction: current and future perspectives
Drug-target Interactions (DTIs) prediction plays a central role in drug discovery.
Computational methods in DTIs prediction have gained more attention because carrying out …
Computational methods in DTIs prediction have gained more attention because carrying out …
Adversarial regressive domain adaptation approach for infrared thermography-based unsupervised remaining useful life prediction
Infrared thermography provides abundant spatiotemporal degradation information,
facilitating non-contact condition monitoring. Reducing domain shift between simulated and …
facilitating non-contact condition monitoring. Reducing domain shift between simulated and …
Joint clustering and discriminative feature alignment for unsupervised domain adaptation
Unsupervised Domain Adaptation (UDA) aims to learn a classifier for the unlabeled target
domain by leveraging knowledge from a labeled source domain with a different but related …
domain by leveraging knowledge from a labeled source domain with a different but related …
Domain adaptation by class centroid matching and local manifold self-learning
Domain adaptation has been a fundamental technology for transferring knowledge from a
source domain to a target domain. The key issue of domain adaptation is how to reduce the …
source domain to a target domain. The key issue of domain adaptation is how to reduce the …
Entropy minimization versus diversity maximization for domain adaptation
Entropy minimization has been widely used in unsupervised domain adaptation (UDA).
However, existing works reveal that the use of entropy-minimization-only may lead to …
However, existing works reveal that the use of entropy-minimization-only may lead to …