DAReN: A Collaborative Approach Towards Reasoning And Disentangling

TL;DR

DAReN introduces an end-to-end framework that jointly learns visual representations and reasoning for RPMs, leveraging a generative model and weak inductive bias to improve performance and disentanglement.

Contribution

It proposes DAReN, a novel joint learning framework based on GM-RPM for visual reasoning and disentanglement, outperforming state-of-the-art models.

Findings

DAReN outperforms SOTA models on benchmark datasets.

Disentangled representations correlate with reasoning success.

Joint learning improves both representation quality and reasoning accuracy.

Abstract

Computational learning approaches to solving visual reasoning tests, such as Raven's Progressive Matrices (RPM), critically depend on the ability 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 introduce 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.