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A class of Wasserstein metrics for probability distributions.
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Cited by
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Generalized Zero- and Few-Shot Learning via Aligned Variational Autoencoders
Schönfeld, E., Ebrahimi, S., Sinha, S., Darrell, T., & Akata, Z. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00844 · Cited by 747
Generalized zero and few-shot transfer for facial forgery detection
Aneja, S., & Nießner, M. (2020) · arXiv · doi:10.48550/arxiv.2006.11863 · Cited by 31
AniGen: Unified S 3 Fields for Animatable 3D Asset Generation
Huang, Y.-H., Zou, Z.-X., He, Y., Chang, C., Pu, C.-F., Yang, Z., Guo, Y.-C., Cao, Y.-P., & Qi, X. (2026) · ACM Transactions on Graphics · doi:10.1145/3811297
“To address this limitation, we incorporate Optimal Transport-based metrics, specifically the Wasserstein distance [Givens and Shortt 1984] and Gromov–Wasserstein (GW) distance [Mémoli 2011], which explicitly account for the global distribution.”
Metadata
- Authors
- Givens, C. R., & Shortt, R. M.
- Venue
- Michigan Mathematical Journal
- Publisher
- Michigan Mathematical Journal
- Published
- 1984-01-01
- Volume
- 31
- Issue
- 2
- ISSN
- 0026-2285 (ISSN-L)
- OpenAlex
- W2040104067
- Cited by
- 559
- References
- 0
- FWCI
- 0.71
- Type
- journal article
- Language
- en
- Open access
- bronze
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- Givens, C. R., & Shortt, R. M. (1984). A class of Wasserstein metrics for probability distributions. Michigan Mathematical Journal. https://doi.org/10.1307/mmj/1029003026
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