# Multi Instance Learning For Unbalanced Data

## TL;DR

This paper investigates the effectiveness of the Single Instance (SI) learning method in unbalanced multi-instance data, revealing its resilience and advantages especially with neural networks, supported by experiments on synthetic and real datasets.

## Contribution

It demonstrates that SI learning is particularly effective in unbalanced settings and less prone to issues with neural networks compared to linear classifiers.

## Key findings

- Larger data imbalance improves SI method resilience.
- Neural networks mitigate known issues of SI in linear classifiers.
- Results validated on synthetic and COCO datasets.

## Abstract

In the context of Multi Instance Learning, we analyze the Single Instance (SI) learning objective. We show that when the data is unbalanced and the family of classifiers is sufficiently rich, the SI method is a useful learning algorithm. In particular, we show that larger data imbalance, a quality that is typically perceived as negative, in fact implies a better resilience of the algorithm to the statistical dependencies of the objects in bags. In addition, our results shed new light on some known issues with the SI method in the setting of linear classifiers, and we show that these issues are significantly less likely to occur in the setting of neural networks. We demonstrate our results on a synthetic dataset, and on the COCO dataset for the problem of patch classification with weak image level labels derived from captions.