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

## TL;DR

This paper provides a theoretical analysis of the generalization error in set-to-set matching tasks using neural networks, addressing a gap in understanding the behavior of such models.

## Contribution

It introduces a novel generalization bound for set-to-set matching with neural networks, incorporating negative sampling techniques.

## Key findings

- Derived a new generalization bound for set-to-set matching models.
- Analyzed the impact of negative sampling on model generalization.
- Provides insights into the theoretical behavior of neural set matching.

## 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.