# Learning Tversky Similarity

arXiv: https://arxiv.org/abs/2006.11372 · HTML: https://mlsanity.com/paper/2006.11372/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 May 2020.

## Built on by (most cited first, 1 of 1)

- [Evaluation of Machine-generated Biomedical Images via A Tally-based Similarity Measure](https://mlsanity.com/paper/2503.22658/) (2025)

## Abstract (arXiv)

In this paper, we advocate Tversky's ratio model as an appropriate basis for computational approaches to semantic similarity, that is, the comparison of objects such as images in a semantically meaningful way. We consider the problem of learning Tversky similarity measures from suitable training data indicating whether two objects tend to be similar or dissimilar. Experimentally, we evaluate our approach to similarity learning on two image datasets, showing that is performs very well compared to existing methods.