mlsanityData to 7 Oct 2026

SHIFT15M: Fashion-specific dataset for set-to-set matching with several distribution shifts

arXiv 2108.12992 ↗First posted 30 Aug 2021cs.LG cs.CVcited by 2

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Abstract (arXiv)

This paper addresses the problem of set-to-set matching, which involves matching two different sets of items based on some criteria, especially in the case of high-dimensional items like images. Although neural networks have been applied to solve this problem, most machine learning-based approaches assume that the training and test data follow the same distribution, which is not always true in real-world scenarios. To address this limitation, we introduce SHIFT15M, a dataset that can be used to evaluate set-to-set matching models when the distribution of data changes between training and testing. We conduct benchmark experiments that demonstrate the performance drop of naive methods due to distribution shift. Additionally, we provide software to handle the SHIFT15M dataset in a simple manner, with the URL for the software to be made available after publication of this manuscript. We believe proposed SHIFT15M dataset provide a valuable resource for evaluating set-to-set matching models under the distribution shift.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 76 of its 169 references that are in the map. First posted on arXiv 30 Aug 2021.

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76 of 169 references.

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Citing papers per year: 2024: 1; 2025: 1.

2 papers.

  1. 2024On permutation-invariant neural networkscited by 10
  2. 2025Advances in Set Function Learning: A Survey of Techniques and Applicationscited by 3