Domain adaptation via incremental confidence samples into classification
Sign Up to like & getrecommendations! Published in 2021 at "International Journal of Intelligent Systems"
DOI: 10.1002/int.22629
Abstract: To accurately recognize similar objects in different domains, the key for domain adaptation is to learn new metrics so as to minimize the discrepancy of two domains. Recent works utilize joint probability domain adaptation to… read more here.
Keywords: confidence samples; incremental confidence; samples classification; domain adaptation ... See more keywords
Bin similarity‐based domain adaptation for fine‐grained image classification
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DOI: 10.1002/int.22775
Abstract: Fine‐grained classification tasks are challenging because fine‐grained data sets are quite scarce. Thus, we utilized the domain adaptation method to migrate knowledge from large, labeled data sets to fine‐grained target data sets. We employed the… read more here.
Keywords: domain adaptation; classification; domain; fine grained ... See more keywords
Self‐supervised domain adaptation for cross‐domain fault diagnosis
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DOI: 10.1002/int.23026
Abstract: Unsupervised domain adaptation‐based fault diagnosis methods have been extensively studied due to their powerful knowledge transferability under different working conditions. Despite their encouraging performance, most of them cannot sufficiently account for the temporal dimension of… read more here.
Keywords: self supervised; domain; fault diagnosis; domain adaptation ... See more keywords
Multiscale unsupervised domain adaptation for automatic pancreas segmentation in CT volumes using adversarial learning.
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DOI: 10.1002/mp.15827
Abstract: PURPOSE Computer-aided automatic pancreas segmentation is essential for early diagnosis and treatment of pancreatic diseases. However, the annotation of pancreas images requires professional doctors and considerable expenditure. Due to imaging differences among various institution population,… read more here.
Keywords: domain adaptation; segmentation model; domain; segmentation ... See more keywords
Deep domain adaptation with manifold aligned label transfer
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DOI: 10.1007/s00138-019-01003-1
Abstract: We propose a novel deep learning domain adaptation method that performs transductive learning from the source domain to the target domain based on cluster matching between the source and target features. The proposed method combines… read more here.
Keywords: target; domain adaptation; adaptation; label transfer ... See more keywords
Unsupervised domain adaptation with target reconstruction and label confusion in the common subspace
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DOI: 10.1007/s00521-018-3846-x
Abstract: Deep neural networks can learn powerful and discriminative representations from a large number of labeled samples. However, it is typically costly to collect and annotate large-scale datasets, which limits the applications of deep learning in… read more here.
Keywords: reconstruction; domain; target; domain adaptation ... See more keywords
Unsupervised double weighted domain adaptation
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DOI: 10.1007/s00521-020-05228-4
Abstract: Domain adaptation can effectively transfer knowledge between domains with different distributions. Most existing methods use distribution alignment to mitigate the domain shift. But they typically align the marginal and conditional distributions with equal weights. This… read more here.
Keywords: weighted domain; domain adaptation; double weighted; domain ... See more keywords
A semi-supervised domain adaptation assembling approach for image classification
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DOI: 10.1007/s10044-017-0664-1
Abstract: Automatic annotation of images is one of the fundamental problems in computer vision applications. With the increasing amount of freely available images, it is quite possible that the training data used to learn a classifier… read more here.
Keywords: classification; semi supervised; domain adaptation; fusion methods ... See more keywords
Domain adaptation network based on hypergraph regularized denoising autoencoder
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DOI: 10.1007/s10462-017-9576-0
Abstract: Domain adaptation learning aims to solve the classification problems of unlabeled target domain by using rich labeled samples in source domain, but there are three main problems: negative transfer, under adaptation and under fitting. Aiming… read more here.
Keywords: denoising autoencoder; network; hypergraph; domain adaptation ... See more keywords
Bi-directional class-wise adversaries for unsupervised domain adaptation
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DOI: 10.1007/s10489-021-02609-7
Abstract: Unsupervised domain adaptation relies on well-labeled auxiliary source domain information to get better performance on the unlabeled target domain. It has shown tremendous importance for various classification and segmentation problems. Classical methods rely on diminishing… read more here.
Keywords: class; class wise; domain adaptation; domain ... See more keywords
CLDA: an adversarial unsupervised domain adaptation method with classifier-level adaptation
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DOI: 10.1007/s11042-020-08877-8
Abstract: Domain adaptation is an active and important research field in transfer learning. Unsupervised domain adaptation, which is better in line with real-world scenarios than supervised and semi-supervised domain adaptation, has attracted much attention and research.… read more here.
Keywords: adversarial unsupervised; domain; level; domain adaptation ... See more keywords