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A review of domain adaptation without target labels
Domain adaptation has become a prominent problem setting in machine learning and
related fields. This review asks the question: How can a classifier learn from a source …
related fields. This review asks the question: How can a classifier learn from a source …
A survey of recent advances in transfer learning
H Liang, W Fu, F Yi - 2019 IEEE 19th international conference …, 2019 - ieeexplore.ieee.org
The integration of transfer learning methods and other machine learning branches can bring
a good improvement in speed and performance, it has become a good research topic in the …
a good improvement in speed and performance, it has become a good research topic in the …
An introduction to domain adaptation and transfer learning
In machine learning, if the training data is an unbiased sample of an underlying distribution,
then the learned classification function will make accurate predictions for new samples …
then the learned classification function will make accurate predictions for new samples …
Stratified transfer learning for cross-domain activity recognition
In activity recognition, it is often expensive and time-consuming to acquire sufficient activity
labels. To solve this problem, transfer learning leverages the labeled samples from the …
labels. To solve this problem, transfer learning leverages the labeled samples from the …
Domain adaptive fake news detection via reinforcement learning
With social media being a major force in information consumption, accelerated propagation
of fake news has presented new challenges for platforms to distinguish between legitimate …
of fake news has presented new challenges for platforms to distinguish between legitimate …
Weighted and class-specific maximum mean discrepancy for unsupervised domain adaptation
Although maximum mean discrepancy (MMD) has achieved great success in unsupervised
domain adaptation (UDA), most of existing UDA methods ignore the issue of class weight …
domain adaptation (UDA), most of existing UDA methods ignore the issue of class weight …
Leveraging imitation learning in agricultural robotics: a comprehensive survey and comparative analysis
Imitation learning (IL), a burgeoning frontier in machine learning, holds immense promise
across diverse domains. In recent years, its integration into robotics has sparked significant …
across diverse domains. In recent years, its integration into robotics has sparked significant …
Unsupervised domain adaptation in activity recognition: A GAN-based approach
Sensor-based human activity recognition (HAR) is having a significant impact in a wide
range of applications in smart city, smart home, and personal healthcare. Such wide …
range of applications in smart city, smart home, and personal healthcare. Such wide …
Unsupervised cross-domain fault diagnosis using feature representation alignment networks for rotating machinery
In this article, the problem of the cross-domain fault diagnosis of rotating machinery is
considered. In a practical setting of this approach, the operating platform of the machine may …
considered. In a practical setting of this approach, the operating platform of the machine may …
Cross-position activity recognition with stratified transfer learning
Human activity recognition (HAR) aims to recognize the activities of daily living by utilizing
the sensors attached to different body parts. HAR relies on the machine learning models …
the sensors attached to different body parts. HAR relies on the machine learning models …