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 …

Artificial intelligence (AI) continues to transform data analysis in many domains. Progress in
each domain is driven by a growing body of annotated data, increased computational …

Machine learning systems generally assume that the training and testing distributions are
the same. To this end, a key requirement is to develop models that can generalize to unseen …

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 …

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 …

Unsupervised domain adaptation (UDA) aims to adapt models learned from a well-
annotated source domain to a target domain, where only unlabeled samples are given …

Due to the data distribution discrepancy, fault diagnosis models, trained with labeled data in
one scene, likely fails in classifying by unlabeled data acquired from the other scenes …

Surface defect recognition using Deep Learning based computer vision techniques is an
important task in industrial manufacturing. However, surface images have different …

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 …