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 …

Deep transfer learning (DTL), which incorporates new ideas from deep neural networks into
transfer learning (TL), has achieved excellent success in computer vision, text 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 …

For a target task where the labeled data are unavailable, domain adaptation can transfer a
learner from a different source domain. Previous deep domain adaptation methods mainly …

Domain adaptation techniques have been widely applied to the problem of cross-scene
hyperspectral image (HSI) classification. Most existing methods use convolutional neural …

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 …

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 …

The recent advances in deep transfer learning reveal that adversarial learning can be
embedded into deep networks to learn more transferable features to reduce the distribution …

Recent studies have shown that deep domain adaptation (DA) techniques have good
performance on cross-domain hyperspectral image (HSI) classification problems. However …

The wide spread of fake news is increasingly threatening both individuals and society. Great
efforts have been made for automatic fake news detection on a single domain (eg, politics) …