EFFECTIVE ABSTRACT REASONING WITH DUAL-CONTRAST NETWORK

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EFFECTIVE ABSTRACT REASONING WITH DUAL-CONTRAST NETWORK
Title:
EFFECTIVE ABSTRACT REASONING WITH DUAL-CONTRAST NETWORK
Journal Title:
2021 International Conference on Learning Representations
Keywords:
Publication Date:
27 May 2021
Citation:
Zhuo, T., & Kankanhalli, M.S. (2022). Effective Abstract Reasoning with Dual-Contrast Network. ICLR 2021.
Abstract:
As a step towards improving the abstract reasoning capability of machines, we aim to solve Raven’s Progressive Matrices (RPM) with neural networks, since solving RPM puzzles is highly correlated with human intelligence. Unlike previous methods that use auxiliary annotations or assume hidden rules to produce appropriate feature representation, we only use the ground truth answer of each question for model learning, aiming for an intelligent agent to have a strong learning capability with a small amount of supervision. Based on the RPM problem formulation, the correct answer filled into the missing entry of the third row/column has to best satisfy the same rules shared between the first two rows/columns. Thus we design a simple yet effective Dual-Contrast Network (DCNet) to exploit the inherent structure of RPM puzzles. Specifically, a rule contrast module is designed to compare the latent rules between the filled row/column and the first two rows/columns; a choice contrast module is designed to increase the relative differences between candidate choices. Experimental results on the RAVEN and PGM datasets show that DCNet outperforms the state-of-the-art methods by a large margin of 5.77%. Further experiments on few training samples and model generalization also show the effectiveness of DCNet. Code is available at https://github.com/visiontao/dcnet.
License type:
Attribution 4.0 International (CC BY 4.0)
Funding Info:
This research / project is supported by the Agency for Science, Technology and Research, Singapore - AME Programmatic Funding Scheme
Grant Reference no. : A18A2b0046
Description:
ISBN:
10.48550/arXiv.2205.13720
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