Generalization Bounds for Set-to-Set Matching with Negative Sampling

Masanari Kimura

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arXiv · 2302.12991

Generalization Bounds for Set-to-Set Matching with Negative Sampling

Masanari Kimura

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Abstract

The problem of matching two sets of multiple elements, namely set-to-set matching, has received a great deal of attention in recent years. In particular, it has been reported that good experimental results can be obtained by preparing a neural network as a matching function, especially in complex cases where, for example, each element of the set is an image. However, theoretical analysis of set-to-set matching with such black-box functions is lacking. This paper aims to perform a generalization error analysis in set-to-set matching to reveal the behavior of the model in that task.

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BibTeX RIS

Masanari Kimura. 2023-02-25. Generalization Bounds for Set-to-Set Matching with Negative Sampling. https://arxiv.org/abs/2302.12991

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