Tree-Sliced Wasserstein Distance: A Geometric Perspective
Hoang V. Tran, Huyen Trang Pham, Tho Tran Huu, Minh-Khoi Nguyen-Nhat, Thanh T. Chu, Tam Le, Tan Minh Nguyen
Abstract
Many variants of Optimal Transport (OT) have been developed to address its heavy computation. Among them, notably, Sliced Wasserstein (SW) is widely used for application domains by projecting the OT problem onto one-dimensional lines, and leveraging the closed-form expression of the univariate OT to reduce the computational burden. However, projecting measures onto lowdimensional spaces can lead to a loss of topological information. To mitigate this issue, in this work, we propose to replace one-dimensional lines with a more intricate structure, called tree systems. This structure is metrizable by a tree metric, which yields a closed-form expression for OT problems on tree systems. We provide an extensive theoretical analysis to formally define tree systems with their topological properties, introduce the concept of splitting maps, which operate as the projection mechanism onto these structures, then finally propose a novel variant of Radon transform for tree systems and verify its injectivity. This framework leads to an efficient metric between measures, termed Tree-Sliced Wasserstein distance on Systems of Lines (TSW-SL). By conducting a variety of experiments on gradient flows, image style transfer, and generative models, we illustrate that our proposed approach performs favorably compared to SW and its variants.
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Install the CLIlune papers fulltext c4bacecc-f31d-4bdf-bfe6-f4e0ecfd1fe5Cited by top-tier papers9
- On Linear Mode Connectivity of Mixture-of-Experts ArchitecturesViet-Hoang Tran, Van-Hoan Trinh, Khanh Vinh Bui, Tan M. NguyenNeurIPS 2025 · 9 citations
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- Adversarial Encoding Perturbation and Synthesis for Set Representation Auxiliary LearningYankai Chen, Xinni Zhang, Henry Peng Zou, Bowei He et al.ICLR 2026
- Tree-sliced Sobolev IPMViet-Hoang Tran, Thanh Q. Tran, Thanh T. Chu, Duy-Tung Pham et al.ICLR 2026
Builds on28
- Denoising Diffusion Probabilistic ModelsJonathan Ho, Ajay Jain, Pieter AbbeelNeurIPS 2020 · 35,902 citations
- Tackling the Generative Learning Trilemma with Denoising Diffusion GANsZhisheng Xiao, Karsten Kreis, Arash VahdatICLR 2022 · 726 citations
- Distributional Sliced-Wasserstein and Applications to Generative ModelingKhai Nguyen, Nhat Ho, Tung Pham, Hung BuiICLR 2021 · 111 citations
- Point-set Distances for Learning Representations of 3D Point CloudsTrung Nguyen, Quang-Hieu Pham, Tam Le, Tung Pham et al.ICCV 2021 · 89 citations
- Fast Approximation of the Sliced-Wasserstein Distance Using Concentration of Random ProjectionsKimia Nadjahi, Alain Durmus, Pierre E. Jacob, Roland Badeau et al.NeurIPS 2021 · 54 citations
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