Fault diagnosis in rotating machines based on transfer learning: Literature review

I Misbah, CKM Lee, KL Keung - Knowledge-Based Systems, 2024 - Elsevier
With the emergence of machine learning methods, data-driven fault diagnosis has gained
significant attention in recent years. However, traditional data-driven diagnosis approaches …

Recent advances in transfer learning for cross-dataset visual recognition: A problem-oriented perspective

J Zhang, W Li, P Ogunbona, D Xu - ACM Computing Surveys (CSUR), 2019 - dl.acm.org
This article takes a problem-oriented perspective and presents a comprehensive review of
transfer-learning methods, both shallow and deep, for cross-dataset visual recognition …

Semi-supervised heterogeneous domain adaptation: Theory and algorithms

Z Fang, J Lu, F Liu, G Zhang - IEEE Transactions on Pattern …, 2022 - ieeexplore.ieee.org
Semi-supervised heterogeneous domain adaptation (SsHeDA) aims to train a classifier for
the target domain, in which only unlabeled and a small number of labeled data are …

Learning from a complementary-label source domain: theory and algorithms

Y Zhang, F Liu, Z Fang, B Yuan… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
In unsupervised domain adaptation (UDA), a classifier for the target domain is trained with
massive true-label data from the source domain and unlabeled data from the target domain …

Unsupervised domain adaptation with and without access to source data for estimating occupancy and recognizing activities in smart buildings

J Dridi, M Amayri, N Bouguila - Building and Environment, 2023 - Elsevier
Energy-efficient buildings have gained increasing interest in the last decades as they
provide optimal energy management. With the emergence of smart homes, many smart tools …

Heterogeneous domain adaptation with structure and classification space alignment

Q Tian, H Sun, C Ma, M Cao, Y Chu… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
Domain adaptation (DA) aims at facilitating the target model training by leveraging
knowledge from related but distribution-inconsistent source domain. Most of the previous DA …

TOHAN: A one-step approach towards few-shot hypothesis adaptation

H Chi, F Liu, W Yang, L Lan, T Liu… - Advances in …, 2021 - proceedings.neurips.cc
In few-shot domain adaptation (FDA), classifiers for the target domain are trained with\emph
{accessible} labeled data in the source domain (SD) and few labeled data in the target …

Clarinet: A one-step approach towards budget-friendly unsupervised domain adaptation

Y Zhang, F Liu, Z Fang, B Yuan, G Zhang… - arXiv preprint arXiv …, 2020 - arxiv.org
In unsupervised domain adaptation (UDA), classifiers for the target domain are trained with
massive true-label data from the source domain and unlabeled data from the target domain …

[PDF][PDF] Transfer learning for cross-dataset recognition: a survey

J Zhang, W Li, P Ogunbona - arXiv preprint arXiv:1705.04396, 2017 - researchgate.net
This paper summarises and analyses the cross-dataset recognition transfer learning
techniques with the emphasis on what kinds of methods can be used when the available …

Online transfer learning for RSV case detection

Y Sun, Y Gao, R Bao, GF Cooper… - 2024 IEEE 12th …, 2024 - ieeexplore.ieee.org
Transfer learning has become a pivotal technique in machine learning and has proven to be
effective in various realworld applications. However, utilizing this technique for classification …