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 survey on negative transfer
Transfer learning (TL) utilizes data or knowledge from one or more source domains to
facilitate learning in a target domain. It is particularly useful when the target domain has very …
facilitate learning in a target domain. It is particularly useful when the target domain has very …
A knowledge dynamic matching unit-guided multi-source domain adaptation network with attention mechanism for rolling bearing fault diagnosis
Z Wu, H Jiang, H Zhu, X Wang - Mechanical Systems and Signal …, 2023 - Elsevier
Most current research on multi-source domain adaptation in bearing fault diagnosis focuses
on training domain-agnostic networks whose parameters are static. However, it is …
on training domain-agnostic networks whose parameters are static. However, it is …
From global to local: Multi-scale out-of-distribution detection
Out-of-distribution (OOD) detection aims to detect “unknown” data whose labels have not
been seen during the in-distribution (ID) training process. Recent progress in representation …
been seen during the in-distribution (ID) training process. Recent progress in representation …
Dynamic instance domain adaptation
Most existing studies on unsupervised domain adaptation (UDA) assume that each domain's
training samples come with domain labels (eg, painting, photo). Samples from each domain …
training samples come with domain labels (eg, painting, photo). Samples from each domain …
Each test image deserves a specific prompt: Continual test-time adaptation for 2d medical image segmentation
Distribution shift widely exists in medical images acquired from different medical centres and
poses a significant obstacle to deploying the pre-trained semantic segmentation model in …
poses a significant obstacle to deploying the pre-trained semantic segmentation model in …
Riemannian representation learning for multi-source domain adaptation
Abstract Multi-Source Domain Adaptation (MSDA) aims at training a classification model that
achieves small target error, by leveraging labeled data from multiple source domains and …
achieves small target error, by leveraging labeled data from multiple source domains and …
A two-stage domain alignment method for multi-source domain fault diagnosis
W Cao, Z Meng, D Sun, J Liu, Y Guan, L Cao, J Li… - Measurement, 2023 - Elsevier
The issue of restricted target domain tags and constrained information offered by a single
source domain in the intelligent fault diagnosis may be successfully resolved by multi-source …
source domain in the intelligent fault diagnosis may be successfully resolved by multi-source …
Multi-source collaborative contrastive learning for decentralized domain adaptation
Unsupervised multi-source domain adaptation aims to obtain a model working well on the
unlabeled target domain by reducing the domain gap between the labeled source domains …
unlabeled target domain by reducing the domain gap between the labeled source domains …
Multi-representation dynamic adaptation network for remote sensing scene classification
In recent years, convolutional neural networks (CNNs) have made significant progress in
remote sensing scene classification (RSSC) tasks. Because obtaining a large number of …
remote sensing scene classification (RSSC) tasks. Because obtaining a large number of …