Fault diagnosis in rotating machines based on transfer learning: Literature review
With the emergence of machine learning methods, data-driven fault diagnosis has gained
significant attention in recent years. However, traditional data-driven diagnosis approaches …
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
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 …
transfer-learning methods, both shallow and deep, for cross-dataset visual recognition …
Semi-supervised heterogeneous domain adaptation: Theory and algorithms
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 …
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
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 …
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
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 …
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 …
knowledge from related but distribution-inconsistent source domain. Most of the previous DA …
TOHAN: A one-step approach towards few-shot hypothesis adaptation
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 …
{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
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 …
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 …
techniques with the emphasis on what kinds of methods can be used when the available …
Online transfer learning for RSV case detection
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 …
effective in various realworld applications. However, utilizing this technique for classification …