Abstract
Nowadays, Deep Neural Networks (DNNs) are fundamental to many vision tasks, including large-scale visual recognition. As the primary goal of the DNNs is to characterize complex boundaries of thousands of classes in a high-dimensional space, it is critical to learn higher-order representations for enhancing nonlinear modeling capability. Recently, a novel method called Quantum-State-based Mapping (QSM) has been proposed to improve the feature calibration ability of the existing attention modules in transfer learning tasks. QSM uses the wave function describing the state of microscopic particles to map the feature vector into the probability space. In essence, QSM introduces a novel higher-order representation to improve the nonlinear capability of the network. In this paper, we extend QSM to Quantum Embedding (QE) for designing new attention modules and Self-Organizing Maps, a class of unsupervised learning methods. We also conducted experiments to validate the effectiveness of QE.
J. Zhou—Co-first author.
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Zhang, J. et al. (2024). Applications of Quantum Embedding in Computer Vision. In: Luo, B., Cheng, L., Wu, ZG., Li, H., Li, C. (eds) Neural Information Processing. ICONIP 2023. Communications in Computer and Information Science, vol 1965. Springer, Singapore. https://doi.org/10.1007/978-981-99-8145-8_14
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