Pairwise Relations Discriminator for Unsupervised Raven's Progressive
  Matrices
  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.
