# Multiple Instance Learning with Bag Dissimilarities

arXiv: https://arxiv.org/abs/1309.5643 · HTML: https://mlsanity.com/paper/1309.5643/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Sep 2013.

## Built on by (most cited first, 5 of 5)

- [Learning to Select Cuts for Efficient Mixed-Integer Programming](https://mlsanity.com/paper/2105.13645/) (2021)
- [Anomaly Clustering: Grouping Images into Coherent Clusters of Anomaly Types](https://mlsanity.com/paper/2112.11573/) (2021)
- [Improving Robustness of Malware Classifiers using Adversarial Strings Generated from Perturbed Latent Representations](https://mlsanity.com/paper/2110.11987/) (2021)
- [Multiple Instance Neural Networks Based on Sparse Attention for Cancer Detection using T-cell Receptor Sequences](https://mlsanity.com/paper/2208.04524/) (2022)
- [FMaMIL: Frequency-Driven Mamba Multi-Instance Learning for Weakly Supervised Lesion Segmentation in Medical Images](https://mlsanity.com/paper/2506.07652/) (2025)

## Abstract (arXiv)

Multiple instance learning (MIL) is concerned with learning from sets (bags) of objects (instances), where the individual instance labels are ambiguous. In this setting, supervised learning cannot be applied directly. Often, specialized MIL methods learn by making additional assumptions about the relationship of the bag labels and instance labels. Such assumptions may fit a particular dataset, but do not generalize to the whole range of MIL problems. Other MIL methods shift the focus of assumptions from the labels to the overall (dis)similarity of bags, and therefore learn from bags directly. We propose to represent each bag by a vector of its dissimilarities to other bags in the training set, and treat these dissimilarities as a feature representation. We show several alternatives to define a dissimilarity between bags and discuss which definitions are more suitable for particular MIL problems. The experimental results show that the proposed approach is computationally inexpensive, yet very competitive with state-of-the-art algorithms on a wide range of MIL datasets.