Domain adaptation is crucial for transferring the knowledge from source labeled CT dataset to target unlabeled MR in abdominal multi-organ segmentation. Meanwhile, it highly desirable avoid high annotation cost related and protect privacy. Therefore, we propose an effective source-free unsupervised domain method cross-modality segmentation without access. The proposed framework comprises two st...

There have been some successful attempts to develop data-driven fault diagnostic methods in recent years. A common assumption most studies is that the data of source and target domains are obtained from same sensor. Nevertheless, because electromechanical actuators may complex motion trajectories mechanical structures, it not always be possible acquire a particular sensor position. When locatio...

We study a realistic domain adaptation setting where one has access to an already existing “black-box” machine learning model. Indeed, in real-life scenarios, efficient pre-trained source predictive model is often available and required be preserved. The solution we propose this problem the asset of providing interpretable target transformation by seeking sparse ordered coordinate-wise feature ...

In actual concrete arch dam engineering scenarios, the dynamic data obtained by health monitoring system of an are incomplete. The acquired typically depend on state structure, that is, whether it is intact or Besides, future environmental loads structure unpredictable. Thus, noise also uncertain. practical engineering, use a damage identification model constructed based incomplete information ...

The success of deep learning has set new benchmarks for many medical image analysis tasks. However, models often fail to generalize in the presence distribution shifts between training (source) data and test (target) data. One method commonly employed counter is domain adaptation: using samples from target learn account shifted distributions. In this work we propose an unsupervised adaptation a...

Impact of the level of supervision on Web-based language model domain adaptation Domain adaptation of a language model aims at re-estimating word sequence probabilities in order to better match the peculiarities of a given broad topic of interest. To achieve this task, a common strategy consists in retrieving adaptation texts from the Internet based on a given domain-representative seed text. I...

We propose online unsupervised domain adaptation (DA), which is performed incrementally as data comes in and is applicable when batch DA is not possible. In a part-of-speech (POS) tagging evaluation, we find that online unsupervised DA performs as well as batch DA.