DAReN: A Collaborative Approach Towards Reasoning And Disentangling

• URL: http://arxiv.org/abs/2109.13156v1
• Date: Mon, 27 Sep 2021 16:10:30 GMT
• Title: DAReN: A Collaborative Approach Towards Reasoning And Disentangling
• Authors: Pritish Sahu, Vladimir Pavlovic
• Abstract summary: We propose an end-to-end joint representation-reasoning learning framework, which leverages a weak form of inductive bias to improve both tasks together. We accomplish this using a novel learning framework Disentangling based Abstract Reasoning Network (DAReN) based on the principles of GM-RPM.
• Score: 27.50150027974947
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Computational learning approaches to solving visual reasoning tests, such as Raven's Progressive Matrices (RPM),critically depend on the ability of the computational approach to identify the visual concepts used in the test (i.e., the representation) as well as the latent rules based on those concepts (i.e., the reasoning). However, learning of representation and reasoning is a challenging and ill-posed task,often approached in a stage-wise manner (first representation, then reasoning). In this work, we propose an end-to-end joint representation-reasoning learning framework, which leverages a weak form of inductive bias to improve both tasks together. Specifically, we propose a general generative graphical model for RPMs, GM-RPM, and apply it to solve the reasoning test. We accomplish this using a novel learning framework Disentangling based Abstract Reasoning Network (DAReN) based on the principles of GM-RPM. We perform an empirical evaluation of DAReN over several benchmark datasets. DAReN shows consistent improvement over state-of-the-art (SOTA) models on both the reasoning and the disentanglement tasks. This demonstrates the strong correlation between disentangled latent representation and the ability to solve abstract visual reasoning tasks.

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