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Multi-Label Contrastive Learning for Abstract Visual Reasoning.

Mikolaj Malkinski, Jacek Mandziuk

    IEEE Transactions on Neural Networks and Learning Systems
    |July 6, 2022
    PubMed
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    This study introduces a novel deep learning approach for abstract reasoning, specifically Raven's Progressive Matrices (RPMs). By combining deep learning with human-like rule identification, the method achieves superior performance on benchmark datasets.

    Area of Science:

    • Artificial Intelligence
    • Cognitive Science
    • Machine Learning

    Background:

    • Abstract reasoning, particularly Raven's Progressive Matrices (RPMs), is a key indicator of human intelligence.
    • Deep learning (DL) models now exceed human performance on RPMs, but through pattern recognition and dataset bias exploitation, not rule-based understanding.
    • A cognitive gap exists between DL and human approaches to solving RPMs.

    Purpose of the Study:

    • To bridge the cognitive gap in abstract reasoning by integrating DL with human-like rule identification for solving RPMs.
    • To develop a novel framework for RPM problem-solving that emphasizes understanding underlying rules rather than solely relying on patterns.

    Main Methods:

    • The problem of solving RPMs is framed as a multilabel classification task, where each RPM is a data point with labels representing its underlying abstract rules.

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  • A generalized noise contrastive estimation algorithm for multilabel samples and a sparse rule encoding scheme are introduced for efficient system training.
  • The proposed approach is evaluated on two benchmark datasets: I-RAVEN and procedurally generated matrices (PGM).

    • Main Results:

    • The developed approach demonstrates an advantage over existing state-of-the-art methods on both the I-RAVEN and PGM datasets.
    • The findings suggest that incorporating rule-based reasoning enhances DL performance on abstract reasoning tasks.

    Conclusions:

    • Combining deep learning with human-like rule identification offers a promising direction for advancing artificial intelligence in abstract reasoning.
    • The proposed multilabel classification framework and efficient training methods provide a robust solution for complex RPM problems.