Transfer learning algorithms for bearing remaining useful life prediction: A comprehensive review from an industrial application perspective
Accurate remaining useful life (RUL) prediction for rolling bearings encounters many
challenges such as complex degradation processes, varying working conditions, and …
challenges such as complex degradation processes, varying working conditions, and …
A comprehensive survey on transfer learning
Transfer learning aims at improving the performance of target learners on target domains by
transferring the knowledge contained in different but related source domains. In this way, the …
transferring the knowledge contained in different but related source domains. In this way, the …
Single-source domain expansion network for cross-scene hyperspectral image classification
Currently, cross-scene hyperspectral image (HSI) classification has drawn increasing
attention. It is necessary to train a model only on source domain (SD) and directly …
attention. It is necessary to train a model only on source domain (SD) and directly …
Graph information aggregation cross-domain few-shot learning for hyperspectral image classification
Most domain adaptation (DA) methods in cross-scene hyperspectral image classification
focus on cases where source data (SD) and target data (TD) with the same classes are …
focus on cases where source data (SD) and target data (TD) with the same classes are …
Adarnn: Adaptive learning and forecasting of time series
Time series has wide applications in the real world and is known to be difficult to forecast.
Since its statistical properties change over time, its distribution also changes temporally …
Since its statistical properties change over time, its distribution also changes temporally …
Topological structure and semantic information transfer network for cross-scene hyperspectral image classification
Domain adaptation techniques have been widely applied to the problem of cross-scene
hyperspectral image (HSI) classification. Most existing methods use convolutional neural …
hyperspectral image (HSI) classification. Most existing methods use convolutional neural …
A survey of unsupervised deep domain adaptation
Deep learning has produced state-of-the-art results for a variety of tasks. While such
approaches for supervised learning have performed well, they assume that training and …
approaches for supervised learning have performed well, they assume that training and …
Language-aware domain generalization network for cross-scene hyperspectral image classification
Text information including extensive prior knowledge about land cover classes has been
ignored in hyperspectral image (HSI) classification tasks. It is necessary to explore the …
ignored in hyperspectral image (HSI) classification tasks. It is necessary to explore the …
Cross-scene wetland mapping on hyperspectral remote sensing images using adversarial domain adaptation network
Wetlands are one of the most important ecosystems on the Earth, and using hyperspectral
remote sensing (RS) technology for fine wetland mapping is important for restoring and …
remote sensing (RS) technology for fine wetland mapping is important for restoring and …
A survey of transfer learning for machinery diagnostics and prognostics
In industrial manufacturing systems, failures of machines caused by faults in their key
components greatly influence operational safety and system reliability. Many data-driven …
components greatly influence operational safety and system reliability. Many data-driven …