The Balanced-Pairwise-Affinities Feature Transform

Daniel Shalam, Simon Korman
Proceedings of the 41st International Conference on Machine Learning, PMLR 235:44342-44357, 2024.

Abstract

The Balanced-Pairwise-Affinities (BPA) feature transform is designed to upgrade the features of a set of input items to facilitate downstream matching or grouping related tasks. The transformed set encodes a rich representation of high order relations between the input features. A particular min-cost-max-flow fractional matching problem, whose entropy regularized version can be approximated by an optimal transport (OT) optimization, leads to a transform which is efficient, differentiable, equivariant, parameterless and probabilistically interpretable. While the Sinkhorn OT solver has been adapted extensively in many contexts, we use it differently by minimizing the cost between a set of features to itself and using the transport plan’s rows as the new representation.Empirically, the transform is highly effective and flexible in its use and consistently improves networks it is inserted into, in a variety of tasks and training schemes. We demonstrate state-of-the-art results in few-shot classification, unsupervised image clustering and person re-identification. Code is available at github.com/DanielShalam/BPA .

Cite this Paper

BibTeX
@InProceedings{pmlr-v235-shalam24a, title = {The Balanced-Pairwise-Affinities Feature Transform}, author = {Shalam, Daniel and Korman, Simon}, booktitle = {Proceedings of the 41st International Conference on Machine Learning}, pages = {44342--44357}, year = {2024}, editor = {Salakhutdinov, Ruslan and Kolter, Zico and Heller, Katherine and Weller, Adrian and Oliver, Nuria and Scarlett, Jonathan and Berkenkamp, Felix}, volume = {235}, series = {Proceedings of Machine Learning Research}, month = {21--27 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v235/main/assets/shalam24a/shalam24a.pdf}, url = {https://proceedings.mlr.press/v235/shalam24a.html}, abstract = {The Balanced-Pairwise-Affinities (BPA) feature transform is designed to upgrade the features of a set of input items to facilitate downstream matching or grouping related tasks. The transformed set encodes a rich representation of high order relations between the input features. A particular min-cost-max-flow fractional matching problem, whose entropy regularized version can be approximated by an optimal transport (OT) optimization, leads to a transform which is efficient, differentiable, equivariant, parameterless and probabilistically interpretable. While the Sinkhorn OT solver has been adapted extensively in many contexts, we use it differently by minimizing the cost between a set of features to itself and using the transport plan’s rows as the new representation.Empirically, the transform is highly effective and flexible in its use and consistently improves networks it is inserted into, in a variety of tasks and training schemes. We demonstrate state-of-the-art results in few-shot classification, unsupervised image clustering and person re-identification. Code is available at github.com/DanielShalam/BPA .} }
Endnote
%0 Conference Paper %T The Balanced-Pairwise-Affinities Feature Transform %A Daniel Shalam %A Simon Korman %B Proceedings of the 41st International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2024 %E Ruslan Salakhutdinov %E Zico Kolter %E Katherine Heller %E Adrian Weller %E Nuria Oliver %E Jonathan Scarlett %E Felix Berkenkamp %F pmlr-v235-shalam24a %I PMLR %P 44342--44357 %U https://proceedings.mlr.press/v235/shalam24a.html %V 235 %X The Balanced-Pairwise-Affinities (BPA) feature transform is designed to upgrade the features of a set of input items to facilitate downstream matching or grouping related tasks. The transformed set encodes a rich representation of high order relations between the input features. A particular min-cost-max-flow fractional matching problem, whose entropy regularized version can be approximated by an optimal transport (OT) optimization, leads to a transform which is efficient, differentiable, equivariant, parameterless and probabilistically interpretable. While the Sinkhorn OT solver has been adapted extensively in many contexts, we use it differently by minimizing the cost between a set of features to itself and using the transport plan’s rows as the new representation.Empirically, the transform is highly effective and flexible in its use and consistently improves networks it is inserted into, in a variety of tasks and training schemes. We demonstrate state-of-the-art results in few-shot classification, unsupervised image clustering and person re-identification. Code is available at github.com/DanielShalam/BPA .
APA
Shalam, D. & Korman, S.. (2024). The Balanced-Pairwise-Affinities Feature Transform. Proceedings of the 41st International Conference on Machine Learning, in Proceedings of Machine Learning Research 235:44342-44357 Available from https://proceedings.mlr.press/v235/shalam24a.html.

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