# Pairwise Relations Discriminator for Unsupervised Raven's Progressive Matrices

arXiv: https://arxiv.org/abs/2011.01306 · HTML: https://mlsanity.com/paper/2011.01306/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Nov 2020.

## Built on by (most cited first, 3 of 3)

- [Deep Learning Methods for Abstract Visual Reasoning: A Survey on Raven's Progressive Matrices](https://mlsanity.com/paper/2201.12382/) (2022)
- [Computational Models of Solving Raven's Progressive Matrices: A Comprehensive Introduction](https://mlsanity.com/paper/2302.04238/) (2023)
- [Deep Non-Monotonic Reasoning for Visual Abstract Reasoning Tasks](https://mlsanity.com/paper/2302.07137/) (2023)

## Abstract (arXiv)

The ability to hypothesise, develop abstract concepts based on concrete observations and apply these hypotheses to justify future actions has been paramount in human development. An existing line of research in outfitting intelligent machines with abstract reasoning capabilities revolves around the Raven's Progressive Matrices (RPM). There have been many breakthroughs in supervised approaches to solving RPM in recent years. However, this process requires external assistance, and thus it cannot be claimed that machines have achieved reasoning ability comparable to humans. Namely, humans can solve RPM problems without supervision or prior experience once the RPM rule that relations can only exist row/column-wise is properly introduced. In this paper, we introduce a pairwise relations discriminator (PRD), a technique to develop unsupervised models with sufficient reasoning abilities to tackle an RPM problem. PRD reframes the RPM problem into a relation comparison task, which we can solve without requiring the labelling of the RPM problem. We can identify the optimal candidate by adapting the application of PRD to the RPM problem. Our approach, the PRD, establishes a new state-of-the-art unsupervised learning benchmark with an accuracy of 55.9% on the I-RAVEN, presenting a significant improvement and a step forward in equipping machines with abstract reasoning.