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Tuia, D.[Devis],
Rakotomamonjy, A.,
Optimal Transport for Domain Adaptation,
PAMI(39), No. 9, September 2017, pp. 1853-1865.
IEEE DOI
1708
BibRef
Earlier: A3, A2, A4, A1:
Multitemporal classification without new labels:
A solution with optimal transport,
MultiTemp15(1-4)
IEEE DOI
1511
Data analysis, Feature extraction, Kernel,
Probability density function, Probability distribution, Training,
Transportation, Unsupervised domain adaptation, classification,
optimal transport, transfer learning, visual adaptation.
geophysical image processing
BibRef
Schmitz, M.A.[Morgan A.],
Heitz, M.[Matthieu],
Bonneel, N.[Nicolas],
Ngolč, F.[Fred],
Coeurjolly, D.[David],
Cuturi, M.[Marco],
Peyre, G.[Gabriel],
Starck, J.L.[Jean-Luc],
Wasserstein Dictionary Learning: Optimal Transport-Based Unsupervised
Nonlinear Dictionary Learning,
SIIMS(11), No. 1, 2018, pp. 643-678.
DOI Link
1804
BibRef
Le, T.N.[Tien-Nam],
Habrard, A.[Amaury],
Sebban, M.[Marc],
Deep multi-Wasserstein unsupervised domain adaptation,
PRL(125), 2019, pp. 249-255.
Elsevier DOI
1909
Domain adaptation, Deep learning, Wasserstein metric, Optimal transport
BibRef
Damodaran, B.B.[Bharath Bhushan],
Flamary, R.[Rémi],
Seguy, V.[Vivien],
Courty, N.[Nicolas],
An Entropic Optimal Transport loss for learning deep neural networks
under label noise in remote sensing images,
CVIU(191), 2020, pp. 102863.
Elsevier DOI
2002
Optimal transport, Entropic Optimal Transport,
Robust deep learning, Noisy labels, Remote sensing
BibRef
Fatras, K.[Kilian],
Damodaran, B.B.[Bharath Bhushan],
Lobry, S.[Sylvain],
Flamary, R.[Rémi],
Tuia, D.[Devis],
Courty, N.[Nicolas],
Wasserstein Adversarial Regularization for Learning With Label Noise,
PAMI(44), No. 10, October 2022, pp. 7296-7306.
IEEE DOI
2209
Noise measurement, Training, Entropy, Neural networks, Task analysis,
Smoothing methods, Semantics, Label noise, optimal transport,
adversarial regularization
BibRef
Damodaran, B.B.[Bharath Bhushan],
Kellenberger, B.[Benjamin],
Flamary, R.[Rémi],
Tuia, D.[Devis],
Courty, N.[Nicolas],
DeepJDOT: Deep Joint Distribution Optimal Transport for Unsupervised
Domain Adaptation,
ECCV18(II: 467-483).
Springer DOI
1810
BibRef
Sim, B.[Byeongsu],
Oh, G.[Gyutaek],
Kim, J.[Jeongsol],
Jung, C.Y.[Chan-Yong],
Ye, J.C.[Jong Chul],
Optimal Transport Driven CycleGAN for Unsupervised Learning in
Inverse Problems,
SIIMS(13), No. 4, 2020, pp. 2281-2306.
DOI Link
2012
BibRef
Zhang, Z.[Zhen],
Wang, M.Z.[Mian-Zhi],
Nehorai, A.[Arye],
Optimal Transport in Reproducing Kernel Hilbert Spaces:
Theory and Applications,
PAMI(42), No. 7, July 2020, pp. 1741-1754.
IEEE DOI
2006
Omparing and matching distributions in reproducing kernel Hilbert spaces.
Kernel, Covariance matrices, Hilbert space, Task analysis, Geometry,
Modeling, Optimal transport, reproducing kernel hilbert spaces,
domain adaptation
BibRef
Zhang, Z.[Zhen],
Wang, M.Z.[Mian-Zhi],
Huang, Y.,
Nehorai, A.[Arye],
Aligning Infinite-Dimensional Covariance Matrices in Reproducing
Kernel Hilbert Spaces for Domain Adaptation,
CVPR18(3437-3445)
IEEE DOI
1812
Kernel, Covariance matrices, Correlation,
Maximum likelihood estimation, Hilbert space, Testing, Computational modeling
BibRef
Xu, B.R.[Bing-Rong],
Zeng, Z.G.[Zhi-Gang],
Lian, C.[Cheng],
Ding, Z.M.[Zheng-Ming],
Few-Shot Domain Adaptation via Mixup Optimal Transport,
IP(31), 2022, pp. 2518-2528.
IEEE DOI
2204
Adaptation models, Training, Numerical models, Couplings,
Feature extraction, Deep learning, Automation, Few-shot learning,
data augmentation
BibRef
Su, B.[Bing],
Wu, Y.[Ying],
Learning Meta-Distance for Sequences by Learning a Ground Metric via
Virtual Sequence Regression,
PAMI(44), No. 1, January 2022, pp. 286-301.
IEEE DOI
2112
Measurement, Learning systems, Training, Optimization,
Neural networks, Machine learning, Kernel, Metric learning,
optimal transport
BibRef
Luo, D.[Dixin],
Xu, H.T.[Hong-Teng],
Carin, L.[Lawrence],
Differentiable Hierarchical Optimal Transport for Robust Multi-View
Learning,
PAMI(45), No. 6, June 2023, pp. 7293-7307.
IEEE DOI
2305
Learning systems, Task analysis, Hospitals, Data models,
Optimization, Diseases, Predictive models, Bi-level optimization,
unaligned multi-view data
BibRef
Gilo, O.[Obsa],
Mathew, J.[Jimson],
Mondal, S.[Samrat],
Kumar-Sanodiya, R.[Rakesh],
Unsupervised sub-domain adaptation using optimal transport,
JVCIR(94), 2023, pp. 103857.
Elsevier DOI
2306
Domain adaptation, Subdomain adaptation,
Sliced wasserstein metric, Optimal transport
BibRef
Jin, L.B.[Lian-Bao],
Lei, D.Y.L.N.[Da-Yu Lang Na],
An Optimal Transport View of Class-Imbalanced Visual Recognition,
IJCV(131), No. 1, January 2023, pp. 2845-2863.
Springer DOI
2310
BibRef
Li, Q.[Qiaonan],
Wen, G.H.[Gui-Hua],
Yang, P.[Pei],
From patch, sample to domain: Capture geometric structures for
few-shot learning,
PR(148), 2024, pp. 110147.
Elsevier DOI
2402
Cross-domain, Few-shot learning, Optimal transport
BibRef
Zhang, J.J.[Jin-Jin],
Liu, J.J.[Jun-Jie],
Li, D.[Debang],
Huang, Q.Y.[Qiu-Yu],
Chen, J.X.[Jia-Xin],
Huang, D.[Di],
OTAMatch: Optimal Transport Assignment With PseudoNCE for
Semi-Supervised Learning,
IP(33), 2024, pp. 4231-4244.
IEEE DOI
2408
Robustness, Predictive models, Noise, Task analysis, Optimization,
Data models, Contrastive learning, Pseudo-labeling,
contrastive learning
BibRef
Klink, P.[Pascal],
d'Eramo, C.[Carlo],
Peters, J.[Jan],
Pajarinen, J.[Joni],
On the Benefit of Optimal Transport for Curriculum Reinforcement
Learning,
PAMI(46), No. 11, November 2024, pp. 7191-7204.
IEEE DOI
2410
Task analysis, Interpolation, Training, Reinforcement learning,
Optimization, Approximation algorithms, Games, optimal transport
BibRef
Thorpe, M.[Matthew],
Park, S.[Serim],
Kolouri, S.[Soheil],
Rohde, G.K.[Gustavo K.],
Slepcev, D.[Dejan],
A Transportation LP Distance for Signal Analysis,
JMIV(59), No. 2, October 2017, pp. 187-210.
Springer DOI
1709
BibRef
Crook, O.M.[Oliver M.],
Cucuringu, M.[Mihai],
Hurst, T.[Tim],
Schönlieb, C.B.[Carola-Bibiane],
Thorpe, M.[Matthew],
Zygalakis, K.C.[Konstantinos C.],
A linear transportation LP distance for pattern recognition,
PR(147), 2024, pp. 110080.
Elsevier DOI
2312
Optimal transport, Linear embedding, Multi-channelled signals
BibRef
Bai, Y.K.[Yi-Kun],
Schmitzer, B.[Bernhard],
Thorpe, M.[Matthew],
Kolouri, S.[Soheil],
Sliced Optimal Partial Transport,
CVPR23(13681-13690)
IEEE DOI
2309
BibRef
Park, S.[Serim],
Thorpe, M.[Matthew],
Representing and Learning High Dimensional Data with the Optimal
Transport Map from a Probabilistic Viewpoint,
CVPR18(7864-7872)
IEEE DOI
1812
Strain, Measurement, Manifolds, Probabilistic logic, Data models, Face,
Deformable models
BibRef
Akbari, A.[Ali],
Awais, M.[Muhammad],
Fatemifar, S.[Soroush],
Kittler, J.V.[Josef V.],
Deep Order-Preserving Learning With Adaptive Optimal Transport
Distance,
PAMI(45), No. 1, January 2023, pp. 313-328.
IEEE DOI
2212
Use relative importance of the labels in learning.
Measurement, Optimization, Estimation, Correlation, Task analysis,
Loss measurement, Training, Optimal transport,
deep order-preserving learning
BibRef
Xu, H.T.[Hong-Teng],
Cheng, M.J.[Min-Jie],
Regularized Optimal Transport Layers for Generalized Global Pooling
Operations,
PAMI(45), No. 12, December 2023, pp. 15426-15444.
IEEE DOI
2311
BibRef
Zhang, Q.[Qian],
Zhang, L.[Lin],
Song, R.[Ran],
Cong, R.M.[Run-Min],
Liu, Y.H.[Yong-Huai],
Zhang, W.[Wei],
Learning Common Semantics via Optimal Transport for Contrastive
Multi-View Clustering,
IP(33), 2024, pp. 4501-4515.
IEEE DOI
2408
Semantics, Contrastive learning, Representation learning,
Correlation, Feature extraction, Vectors, Training,
contrastive learning
BibRef
Li, S.H.[Sheng-Hao],
Li, Z.Z.[Ze-Zeng],
Wang, Z.P.[Zhan-Peng],
Xu, Z.B.[Ze-Bin],
Lei, N.[Na],
Luo, Z.X.[Zhong-Xuan],
Measure-Driven Neural Solver for Optimal Transport Mapping,
CirSysVideo(34), No. 10, October 2024, pp. 10437-10448.
IEEE DOI
2411
Task analysis, Classification algorithms, Costs, Neural networks,
Data models, Computational modeling, flexibility
BibRef
Montesuma, E.F.[Eduardo Fernandes],
Mboula, F.M.N.[Fred Maurice Ngolč],
Souloumiac, A.[Antoine],
Recent Advances in Optimal Transport for Machine Learning,
PAMI(47), No. 2, February 2025, pp. 1161-1180.
IEEE DOI
2501
Measurement, Machine learning, Probability distribution, Surveys,
Reinforcement learning, Geometry, Optimization, Lips, Interpolation,
policy optimization
BibRef
Truong, T.D.[Thanh-Dat],
Chappa, R.T.N.[Ravi Teja Nvs],
Nguyen, X.B.[Xuan-Bac],
Le, N.[Ngan],
Dowling, A.P.G.[Ashley P.G.],
Luu, K.[Khoa],
OTAdapt: Optimal Transport-based Approach For Unsupervised Domain
Adaptation,
ICPR22(2850-2856)
IEEE DOI
2212
Measurement, Webcams, Insects, Topology, Proposals
BibRef
Mehra, A.[Akshay],
Zhang, Y.[Yunbei],
Hamm, J.[Jihun],
Test-time Assessment of a Model's Performance on Unseen Domains via
Optimal Transport,
FaDE-TCV24(173-182)
IEEE DOI
2410
Measurement, Training, Correlation, Computational modeling,
Training data, Computer architecture, Predictive models,
Transferability Estimation
BibRef
Lohit, S.[Suhas],
Jones, M.[Michael],
Model Compression Using Optimal Transport,
WACV22(3645-3654)
IEEE DOI
2202
Knowledge engineering, Training, Deep learning,
Image coding, Computational modeling, Mobile handsets,
Learning and Optimization
BibRef
Aburidi, M.[Mohammed],
Marcia, R.[Roummel],
CLOT: Contrastive Learning-Driven and Optimal Transport-Based
Training for Simultaneous Clustering,
ICIP23(1515-1519)
IEEE DOI
2312
BibRef
Zhu, Y.[Yanan],
Qi, Y.Y.[Yang-Yang],
Domain Adaptation based on Attention-Weighted Optimal Transport and
Cluster Alignment,
CVIDL23(514-517)
IEEE DOI
2403
Measure differences in distributions.
Training, Deep learning, Adaptation models, Clustering algorithms,
Cost function, Robustness, clustering alignment
BibRef
Roheda, S.[Siddharth],
Panahi, A.[Ashkan],
Krim, H.[Hamid],
Fast Optimal Transport for Latent Domain Adaptation,
ICIP23(1810-1814)
IEEE DOI
2312
BibRef
Feng, C.[Chuanwen],
Ren, Y.L.[Yi-Long],
Xie, X.[Xike],
OT-Filter: An Optimal Transport Filter for Learning with Noisy Labels,
CVPR23(16164-16174)
IEEE DOI
2309
BibRef
Lu, F.[Fan],
Zhu, K.[Kai],
Zhai, W.[Wei],
Zheng, K.[Kecheng],
Cao, Y.[Yang],
Uncertainty-Aware Optimal Transport for Semantically Coherent
Out-of-Distribution Detection,
CVPR23(3282-3291)
IEEE DOI
2309
BibRef
Yang, J.[Jiechao],
Liu, Y.[Yong],
Xu, H.T.[Hong-Teng],
HOTNAS: Hierarchical Optimal Transport for Neural Architecture Search,
CVPR23(11990-12000)
IEEE DOI
2309
BibRef
Luo, Y.W.[You-Wei],
Ren, C.X.[Chuan-Xian],
MOT: Masked Optimal Transport for Partial Domain Adaptation,
CVPR23(3531-3540)
IEEE DOI
2309
BibRef
An, D.S.[Dong-Sheng],
Xie, J.W.[Jian-Wen],
Li, P.[Ping],
Learning Deep Latent Variable Models by Short-Run MCMC Inference with
Optimal Transport Correction,
CVPR21(15410-15419)
IEEE DOI
2111
Training, Monte Carlo methods, Costs, Image synthesis,
Markov processes, Task analysis
BibRef
Wang, W.L.[Wen-Lin],
Xu, H.T.[Hong-Teng],
Wang, G.Y.[Guo-Yin],
Wang, W.Q.[Wen-Qi],
Carin, L.[Lawrence],
Zero-Shot Recognition via Optimal Transport,
WACV21(3470-3480)
IEEE DOI
2106
Benchmark testing, Generators, Standards
BibRef
Serrurier, M.[Mathieu],
Mamalet, F.[Franck],
González-Sanz, A.[Alberto],
Boissin, T.[Thibaut],
Loubes, J.M.[Jean-Michel],
del Barrio, E.[Eustasio],
Achieving robustness in classification using optimal transport with
hinge regularization,
CVPR21(505-514)
IEEE DOI
2111
Computational modeling, Transportation,
Estimation, Fasteners, Robustness
BibRef
Bouniot, Q.[Quentin],
Audigier, R.[Romaric],
Loesch, A.[Angelique],
Optimal Transport as a Defense Against Adversarial Attacks,
ICPR21(5044-5051)
IEEE DOI
2105
Training, Deep learning, Measurement, Adaptation models,
Perturbation methods, Image representation
BibRef
Taherkhani, F.[Fariborz],
Dabouei, A.[Ali],
Soleymani, S.[Sobhan],
Dawson, J.[Jeremy],
Nasrabadi, N.M.[Nasser M.],
Transporting Labels via Hierarchical Optimal Transport for
Semi-supervised Learning,
ECCV20(IV:509-526).
Springer DOI
2011
BibRef
Xu, R.,
Liu, P.,
Wang, L.,
Chen, C.,
Wang, J.,
Reliable Weighted Optimal Transport for Unsupervised Domain
Adaptation,
CVPR20(4393-4402)
IEEE DOI
2008
Reliability, Kernel, Training, Generators, Task analysis,
Measurement uncertainty
BibRef
Avraham, G.[Gil],
Zuo, Y.[Yan],
Drummond, T.[Tom],
Parallel Optimal Transport GAN,
CVPR19(4406-4415).
IEEE DOI
2002
BibRef
Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Decision Fusion .