A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Filters
A Comparative Study on Unsupervised Domain Adaptation Approaches for Coffee Crop Mapping
[article]
2018
arXiv
pre-print
Preserved Fulltext
In this work, we investigate the application of existing unsupervised domain adaptation (UDA) approaches to the task of transferring knowledge between crop regions having different coffee patterns. ...
However, UDA methods may lead to negative transfer, which may indicate that domains are too different that transferring knowledge is not appropriate. ...
Ideally, we would like to learn a proper domain adaptation in an unsupervised manner. ...
arXiv:1806.02400v1
fatcat:hgnr5k7zeffalojml2ky5wz4ay
Category-Sensitive Domain Adaptation for Land Cover Mapping in Aerial Scenes
2019
Remote Sensing
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
As an efficient solution for this issue, domain adaptation has been widely utilized in numerous semantic labeling-based applications. ...
adaptation (CsDA) method for land cover mapping using very-high-resolution (VHR) optical aerial images. ...
[39] proposed for supervised domain adaptation with paired images, while Zhu et al. [18] p for unsupervised domain adaptation with unpaired images. ...
doi:10.3390/rs11222631
fatcat:gbkmgj7qe5dhjmg44uiqptghbu
Unsupervised Alignment of Distributional Word Embeddings
[article]
2022
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
In this paper, we propose stochastic optimization approach for aligning probabilistic embeddings. ...
Finally, we evaluate our method on the problem of unsupervised word translation, by aligning word embeddings trained on monolingual data. ...
Acknowledgments This work has been supported by the German Research Foundation as part of the Research Training Group Adaptive Preparation of Information from Heterogeneous Sources (AIPHES) under grant ...
arXiv:2203.04863v1
fatcat:zqltrlwnqzgnxbnk5wmq4os6su
Unsupervised Domain Adaptive Graph Convolutional Networks
2020
Proceedings of The Web Conference 2020
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
In this paper, we present a novel approach, unsupervised domain adaptive graph convolutional networks (UDA-GCN), for domain adaptation learning for graphs. ...
However, most GCNs only work in a single domain (graph) incapable of transferring knowledge from/to other domains (graphs), due to the challenges in both graph representation learning and domain adaptation ...
In this paper, we presented a novel unsupervised domain adaptive graph convolutional networks (UDA-GCN) to enable knowledge adaptation between graphs. ...
doi:10.1145/3366423.3380219
dblp:conf/www/WuP0CZ20
fatcat:5rrk25dpd5advc5daunmm4oyza
Learning a Domain-Invariant Embedding for Unsupervised Domain Adaptation Using Class-Conditioned Distribution Alignment
[article]
2019
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Other Versions
We address the problem of unsupervised domain adaptation (UDA) by learning a cross-domain agnostic embedding space, where the distance between the probability distributions of the two source and target ...
To mitigate the challenge of class matching, we also align corresponding classes in the embedding space by using high confidence pseudo-labels for the target domain, i.e. assigning the class for which ...
Fig. 1 : 1 Architecture of the proposed unsupervised domain adaptation framework. ...
arXiv:1907.02271v2
fatcat:2ct3ilhgenetlmtqncjylk5nxm
Spatial-Aware GAN for Unsupervised Person Re-identification
[article]
2021
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Note that this fulltext copy is not of the "primary" version of this work. The version it corresponds to is:
2019 pre-print arXiv:1911.11312v1 fatcat:z35zwy67lnacflzrgq33zmh6pm
Unsupervised domain adaptation (UDA) has been investigated to mitigate this constraint, but most existing systems adapt images at pixel level only and ignore obvious discrepancies at spatial level. ...
A novel disentangled cycle-consistency loss is designed which guides the learning of spatial-level and pixel-level adaptation in a collaborative manner. ...
One typical strategy to tackle domain shift and bias is Unsupervised Domain Adaptation (UDA) which aims to transfer the learned knowledge from a labeled source domain to an unlabeled target domain. ...
arXiv:1911.11312v2
fatcat:5ujkfqunjjfornlzj7xvqdwf74
Emerging Trends in Federated Learning: From Model Fusion to Federated X Learning
[article]
2021
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
Other Versions
Specifically, we explore various learning algorithms to improve the vanilla federated averaging algorithm and review model fusion methods such as adaptive aggregation, regularization, clustered methods ...
emerging trends, we also discuss federated learning in the intersection with other learning paradigms, termed as federated x learning, where x includes multitask learning, meta-learning, transfer learning, unsupervised ...
Fully unsupervised data can be enhanced via domain adaption, where the aim is to transfer knowledge from a labeled domain to an unlabeled one. Taxonomy. ...
arXiv:2102.12920v2
fatcat:5fcwfhxibbedbcbuzrfyqdedky
Mutual-GAN: Towards Unsupervised Cross-Weather Adaptation with Mutual Information Constraint
[article]
2021
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
The proposed Mutual-GAN adopts mutual information constraint to preserve image-objects during cross-weather adaptation, which is an unsolved problem for most unsupervised image-to-image translation approaches ...
Convolutional neural network (CNN) have proven its success for semantic segmentation, which is a core task of emerging industrial applications such as autonomous driving. ...
recently proposed GAN for imagebased domain adaptation, i.e., AugGAN [15] , is not involved for comparison, due to the strong prior-knowledge (pixel-wise annotation) used in the approach. ...
arXiv:2106.16000v1
fatcat:wwjxeu5obnd2vhumi55r77kroq
RefRec: Pseudo-labels Refinement via Shape Reconstruction for Unsupervised 3D Domain Adaptation
[article]
2021
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
Unsupervised Domain Adaptation (UDA) for point cloud classification is an emerging research problem with relevant practical motivations. ...
on both domains; ii) a novel self-training protocol that learns domain-specific decision boundaries and reduces the negative impact of mislabelled target samples and in-domain intra-class variability. ...
Acknowledgment The authors would like to thank Injenia Srl for supporting this research. ...
arXiv:2110.11036v1
fatcat:rror4ona5nh3pag2l7qrilqpme
DiReT: An effective discriminative dimensionality reduction approach for multi-source transfer learning
2017
Scientia Iranica. International Journal of Science and Technology
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2018; you can also visit the original URL.
The file type is application/pdf.
Moreover, DiReT employs multiple source domains and semi-supervised target domain to transfer knowledge from multiple resources, and it also bridges across source and target domains to nd common knowledge ...
KEYWORDS Multi-source transfer learning; Domain adaptation; Discriminative dimensionality reduction; Fisher discriminant analysis. Abstract. ...
In unsupervised domain adaptation, no label is available in target domain. Blitzer et al. ...
doi:10.24200/sci.2017.4113
fatcat:el3vzjxe3nf4vcnlvdvyn6hdvi
Transductive Domain Adaptation with Affinity Learning
2015
Proceedings of the 24th ACM International on Conference on Information and Knowledge Management - CIKM '15
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2017; you can also visit the original URL.
The file type is application/pdf.
We study the problem of domain adaptation, which aims to adapt the classifiers trained on a labeled source domain to an unlabeled target domain. ...
We propose a novel method to solve domain adaptation task in a transductive setting. ...
CONCLUSIONS We propose a novel transductive domain adaptation method. Empirical results clearly demonstrate that it outperforms state-of-the-art methods. ...
doi:10.1145/2806416.2806643
dblp:conf/cikm/ShuL15
fatcat:ld3c5iu6wrhpfgrmpy74xgjjay
Unsupervised Deep Feature Transfer for Low Resolution Image Classification
[article]
2019
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Other Versions
We use pre-trained convenet to extract features for both high- and low-resolution images, and then feed them into a two-layer feature transfer network for knowledge transfer. ...
In this paper, we propose a simple while effective unsupervised deep feature transfer algorithm for low resolution image classification. No fine-tuning on convenet filters is required in our method. ...
[24] propose a deep adaptation network architecture to match the mean embeddings of different domain distributions in a reproducing kernel Hilbert space. Guo et al. ...
arXiv:1908.10012v2
fatcat:3eyozf2fpbgonaf7flqa4bsehm
Multi-domain Adaptation in Brain MRI Through Paired Consistency and Adversarial Learning
[chapter]
2019
Lecture Notes in Computer Science
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
The proposed method significantly outperforms other domain adaptation baselines. ...
Recent work in domain adaptation addresses this challenge and successfully leverages labeled data in a source domain to perform well on an unlabeled target domain. ...
domain adaptation [13] as well as an unsupervised baseline for WMH segmentation. ...
doi:10.1007/978-3-030-33391-1_7
pmid:34109324
pmcid:PMC7610933
fatcat:nfvxm6muvfcn7c6hsv3trl3dry
Heterogeneous Transfer Learning: An Unsupervised Approach
[article]
2018
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Other Versions
The unsupervised knowledge transfer theorem sets out the transfer conditions necessary to prevent negative transfer. ...
Transfer learning leverages the knowledge in one domain, the source domain, to improve learning efficiency in another domain, the target domain. ...
), and unsupervised domain adaptation (HeUDA). ...
arXiv:1701.02511v4
fatcat:annryoyrp5h6dggjnlzn4mcm7y
GRAFT: Unsupervised Adaptation to Resizing for Detection of Image Manipulation
2020
IEEE Access
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Adaptation is performed in an unsupervised fashion, i.e., without using any ground-truth label in the pre-resized testing domain, for the detection of image manipulation on very small patches. ...
We propose a new and effective adaptation method for one state-of-the-art image manipulation detection pipeline, and we call our proposed method Gaussian mixture model Resizing Adaptation by Fine-Tuning ...
Patrick Bas for the discussions on cover-source mismatch for steganalysis [4] and Dr. Pedro Rodrigues for inspiring discussions about his work on Riemannian geometry [25] . ...
doi:10.1109/access.2020.2980992
fatcat:ucx37j3pxfeenmlvyqz3mzodje
« Previous
Showing results 1 — 15 out of 6,500 results