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Curriculum Manager for Source Selection in Multi-Source Domain Adaptation
[article]
2020
arXiv
pre-print
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In this paper, we proposed an adversarial agent that learns a dynamic curriculum for source samples, called Curriculum Manager for Source Selection (CMSS). ...
The performance of Multi-Source Unsupervised Domain Adaptation depends significantly on the effectiveness of transfer from labeled source domain samples. ...
Conclusion In this paper, we proposed Curriculum Manager for Source Selection (CMSS) that learns a curriculum for Multi-Source Unsupervised Domain Adaptation. ...
arXiv:2007.01261v1
fatcat:nrgf2sbqa5a7pcrqfkgcr6qpaq
Curriculum CycleGAN for Textual Sentiment Domain Adaptation with Multiple Sources
[article]
2021
arXiv
pre-print
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Existing multi-source domain adaptation (MDA) methods either fail to extract some discriminative features in the target domain that are related to sentiment, neglect the correlations of different sources ...
with curriculum instance-level adaptation which bridges the gap across source and target domains, and (3) task classifier trained on the intermediate domain for final sentiment classification. ...
, U1933114), Natural Science Foundation of Tianjin, China (Nos. 20JCJQJC00020, 18JCYBJC15400, 18ZXZNGX00110), and the Fundamental Research Funds for the Central Universities. ...
arXiv:2011.08678v2
fatcat:7gyajhdcynblvjgtcjcjrpgy4i
Class-Conditional Domain Adaptation on Semantic Segmentation
[article]
2019
arXiv
pre-print
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Unsupervised domain adaptation can potentially address these problems, allowing systems trained on labelled datasets from one or more source domains (including less expensive synthetic domains) to be adapted ...
We address this problem by introducing a Class-Conditional Domain Adaptation method (CCDA). It includes a class-conditional multi-scale discriminator and the class-conditional loss. ...
Acknowledgements: We would like to thank the York University Vision: Science to Applications (VISTA) program for its support. ...
arXiv:1911.11981v2
fatcat:nx5orxg7sffybk3r2nex2vztay
Unsupervised Domain Adaptation for Semantic Image Segmentation: a Comprehensive Survey
[article]
2021
arXiv
pre-print
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, domain generalization, test-time adaptation or source-free domain adaptation; we conclude this survey by describing datasets and benchmarks most widely used in semantic segmentation research. ...
We present the most important semantic segmentation methods; we provide a comprehensive survey on domain adaptation techniques for semantic segmentation; we unveil newer trends such as multi-domain learning ...
Curriculum Manager for Source Selection in
domain adaptation method for semantic segmentation. In Multi-Source Domain Adaptation. ...
arXiv:2112.03241v1
fatcat:uzlehddvuvfwzf4dfbjimja45e
Lifelong Personalization via Gaussian Process Modeling for Long-Term HRI
2021
Frontiers in Robotics and AI
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Inspired by the way in which agents use active learning to select new training data based on domain context, we augment a Gaussian Process-based multitask personalization model with a mechanism to actively ...
Across a wide variety of domains, artificial agents that can adapt and personalize to users have potential to improve and transform how social services are provided. ...
"short, well-defined lessons delivered with limited adaptation to individual learners or flexibility in curriculum." ...
doi:10.3389/frobt.2021.683066
pmid:34164437
pmcid:PMC8215502
fatcat:wetgbxbkbnconkcfkwnhy3jcxi
Curriculum-style Local-to-global Adaptation for Cross-domain Remote Sensing Image Segmentation
[article]
2022
arXiv
pre-print
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The proposed curriculum-style adaptation performs the adaptation process in an easy-to-hard way according to the adaptation difficulties that can be obtained using an entropy-based score for each patch ...
To address these challenges, we propose a curriculum-style local-to-global cross-domain adaptation framework for the segmentation of VHR RSIs. ...
First, the given source-domain model is adapted from the source domain to the selected easy-to-adapt patches of the target domain. ...
arXiv:2203.01539v1
fatcat:6ostmgkl5ndhzkazasair2pvg4
Page 34 of Communication Abstracts Vol. 22, Issue 1
[page]
1999
Communication Abstracts
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Emergency management of chemical spills was selected to exemplify the rule-based decision task. An expert system in this domain was developed to serve as the training tool. ...
To con- tribute to the human success in playing such a role, this study examines the effec- tiveness of using expert systems to train for the time-constrained decision domain. ...
Medical Image Classification Based on Curriculam Learning
2019
International journal of recent technology and engineering
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This paper is an attempt to apply SSL through Multi-Modal Curriculum Learning (MMCL) strategy over medical images. Through this, medical images can be categorized into normal and abnormal images. ...
Experimental results demonstrate good accuracy for classification. ...
For this purpose binary selection matrix is used and it ensures each image is selected only once. To improve the performance, single modal curriculum is expanded to multi-modal curriculum. ...
doi:10.35940/ijrte.b1001.0782s219
fatcat:l725rxtlqjezfg6lfp6bg2646e
Towards a Capabilities Taxonomy for Prognostics and Health Management
2020
International Journal of Prognostics and Health Management
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This communication proposes the development by the PHM Society of a classification or taxonomy for the skills needed for the prognostics and health management (PHM) field. ...
Preliminary results of the development of Analytics, Test and Experiment Design and Cost Benefit Studies sub-domains within the PHM field are reported based on workshops at the PHM 2012 and 2013 Annual ...
She specializes in curriculum development for adult learning and training in industrial and professional settings. ...
doi:10.36001/ijphm.2014.v5i1.2201
fatcat:3r4md4pi5je5la3qqovlzn76va
Unsupervised Domain Adaptation in Semantic Segmentation: a Review
[article]
2020
arXiv
pre-print
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The aim of this paper is to give an overview of the recent advancements in the Unsupervised Domain Adaptation (UDA) of deep networks for semantic segmentation. ...
analysis of the classifier discrepancies, self-teaching, entropy minimization, curriculum learning and multi-task learning. ...
Multi-Tasking Some works exploit additional types of information available in the source domain dataset, e.g., depth maps, to improve the performance in the target domain. ...
arXiv:2005.10876v1
fatcat:7t5v6qibxnfcxhwtohqqunhd2u
Learning and Knowledge Transfer with Memory Networks for Machine Comprehension
2017
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers
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Motivated by these practical issues, we propose a novel curriculum inspired training procedure for Memory Networks to improve the performance for machine comprehension with relatively small volumes of ...
Additionally, we explore various training regimes for Memory Networks to allow knowledge transfer from a closely related domain having larger volumes of labelled data. ...
in the source domain N SD . ...
doi:10.18653/v1/e17-1080
dblp:conf/eacl/YadavVS17
fatcat:q7r7uklx6zgopmjudrtgu3wfn4
Edison Data Science Framework: Part 3. Data Science Model Curriculum (Mc-Ds) Release 1
2016
Zenodo
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When coupled with individual or group competence benchmarking, MC-DS can also be used for building individual training curricula and professional (self/up) skilling for effective career management. ...
Further MC-DS refinement will be done based on consultation with the universities community and experts both in Data Science and scientific or industry domains. ...
or evaluating the existing curriculum for compliance to the selected Data Science professional profiles. ...
doi:10.5281/zenodo.167592
fatcat:axjcv7zj6jffxobrj2o52624zi
Unsupervised Domain Adaptation in Semantic Segmentation: A Review
2020
Technologies
Preserved Fulltext
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The aim of this paper is to give an overview of the recent advancements in the Unsupervised Domain Adaptation (UDA) of deep networks for semantic segmentation. ...
analysis of the classifier discrepancies, self-teaching, entropy minimization, curriculum learning and multi-task learning. ...
Multi-Tasking Some works exploit additional types of information available in the source domain dataset, for example, depth maps, to improve the performance in the target domain. ...
doi:10.3390/technologies8020035
fatcat:qzgjjiw5p5bldk76mh3s3pwlfq
Zero-Shot Deep Domain Adaptation
[article]
2018
arXiv
pre-print
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Domain adaptation is an important tool to transfer knowledge about a task (e.g. classification) learned in a source domain to a second, or target domain. ...
Therefore, the source-domain task of interest solution (e.g. a classifier for classification tasks) which is jointly trained with the source-domain representation can be applicable to both the source and ...
[17] in a typical domain adaptation (DA) task, where source-domain training data, target-domain training data, and a task of interest (TOI) are given. ...
arXiv:1707.01922v5
fatcat:4zl3hiiegjbtxjh523de4xn6dq
KD3A: Unsupervised Multi-Source Decentralized Domain Adaptation via Knowledge Distillation
[article]
2021
arXiv
pre-print
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Conventional unsupervised multi-source domain adaptation (UMDA) methods assume all source domains can be accessed directly. ...
In this study, we propose a privacy-preserving UMDA paradigm named Knowledge Distillation based Decentralized Domain Adaptation (KD3A), which performs domain adaptation through the knowledge distillation ...
selection
based methods, i.e. the curriculum manager (CMSS) (Yang
et al., 2020). (4) Decentralized UMDA, i.e. ...
arXiv:2011.09757v7
fatcat:h3elgrahsvdf7mt37n3miu2vw4
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