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reasoning ability is fundamental to human intelligence. It enableshumans to uncover relations among abstract concepts and further deduce implicitrules from the relations. As a well-known abstract visual reasoning task,Raven's Progressive Matrices (RPM) are widely used in human IQ tests. Althoughextensive research has been conducted on RPM solvers with machine intelligence,few studies have considered further advancing the standard answer-selection(classification) problem to a more challenging answer-painting (generating)problem, which can verify whether the model has indeed understood the implicitrules. In this paper we aim to solve the latter one by proposing a deep latentvariable model, in which multiple Gaussian processes are employed as priors oflatent variables to separately learn underlying abstract concepts from RPMs;thus the proposed model is interpretable in terms of concept-specific latentvariables. The latent Gaussian process also provides an effective way ofextrapolation for answer painting based on the learned concept-changing rules.We evaluate the proposed model on RPM-like datasets with multiplecontinuously-changing visual concepts. Experimental results demonstrate thatour model requires only few training samples to paint high-quality answers,generate novel RPM panels, and achieve interpretability throughconcept-specific latent variables.

Raven's Progressive Matrices Completion with Latent Gaussian Process Priors