14.1.14.2.8 Optimal Transport

Chapter Contents (Back)
Optimal Transport. Optimal transport (OT) studies the most economical transformation of one probability measure into another.
See also Domain Adaptation.
See also Unsupervised Domain Adaptation.

Courty, N.[Nicolas], Flamary, R.[Rémi], 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


Li, H.X.[Hong-Xia], Huang, W.[Wei], Wang, J.Y.[Jing-Ya], Shi, Y.[Ye],
Global and Local Prompts Cooperation via Optimal Transport for Federated Learning,
CVPR24(12151-12161)
IEEE DOI 2410
Training, Learning systems, Visualization, Federated learning, Design methodology, Collaboration, Data visualization, Optimal Transport 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 .


Last update:Jan 20, 2025 at 11:36:25