Towards Generative Abstract Reasoning: Completing Raven's Progressive Matrix via Rule Abstraction and Selection
CoRR(2024)
摘要
Endowing machines with abstract reasoning ability has been a long-term
research topic in artificial intelligence. Raven's Progressive Matrix (RPM) is
widely used to probe abstract visual reasoning in machine intelligence, where
models need to understand the underlying rules and select the missing
bottom-right images out of candidate sets to complete image matrices. The
participators can display powerful reasoning ability by inferring the
underlying attribute-changing rules and imagining the missing images at
arbitrary positions. However, existing solvers can hardly manifest such an
ability in realistic RPM problems. In this paper, we propose a conditional
generative model to solve answer generation problems through Rule AbstractIon
and SElection (RAISE) in the latent space. RAISE encodes image attributes as
latent concepts and decomposes underlying rules into atomic rules by means of
concepts, which are abstracted as global learnable parameters. When generating
the answer, RAISE selects proper atomic rules out of the global knowledge set
for each concept and composes them into the integrated rule of an RPM. In most
configurations, RAISE outperforms the compared generative solvers in tasks of
generating bottom-right and arbitrary-position answers. We test RAISE in the
odd-one-out task and two held-out configurations to demonstrate how learning
decoupled latent concepts and atomic rules helps find the image breaking the
underlying rules and handle RPMs with unseen combinations of rules and
attributes.
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关键词
Deep Latent Variable Models,Generative Models,Raven’s Progressive Matrix,Abstract Visual Reasoning
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