mlsanityData to 7 Oct 2026

Quaternion Convolutional Neural Networks: Current Advances and Future Directions

arXiv 2307.08663 ↗First posted 17 Jul 2023cs.AI cs.CVcited by 3

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Abstract (arXiv)

Since their first applications, Convolutional Neural Networks (CNNs) have solved problems that have advanced the state-of-the-art in several domains. CNNs represent information using real numbers. Despite encouraging results, theoretical analysis shows that representations such as hyper-complex numbers can achieve richer representational capacities than real numbers, and that Hamilton products can capture intrinsic interchannel relationships. Moreover, in the last few years, experimental research has shown that Quaternion-Valued CNNs (QCNNs) can achieve similar performance with fewer parameters than their real-valued counterparts. This paper condenses research in the development of QCNNs from its very beginnings. We propose a conceptual organization of current trends and analyze the main building blocks used in the design of QCNN models. Based on this conceptual organization, we propose future directions of research.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 51 of its 176 references that are in the map. First posted on arXiv 17 Jul 2023.

Builds on

51 of 176 references.

References · 51

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38 more
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50 most cited of 51 All 51 (JSON)

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 2; 2026: 1.

3 papers.

  1. 2025Quaternion Approximation Networks for Enhanced Image Classification and Oriented Object Detectioncited by 1
  2. 2025Compressing Quaternion Convolutional Neural Networks for Audio Classificationcited by 0
  3. 2026A Family of Quaternion-Valued Differential Evolution Algorithms for Numerical Function Optimizationcited by 0