用于多类图像分类的多模态卷积和卷积神经网络

Yuri G. Gordienko, Yevhenii Trochun, S. Stirenko
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摘要

通过利用混合量子-古典神经网络(HNN),这项研究旨在提高图像分类任务的效率。HNNs 使我们能够利用量子计算来解决机器学习问题,与经典操作相比,HNNs 非常省电,并能显著提高计算速度。这与可持续应用尤其相关,因为减少计算资源和能源消耗至关重要。本研究利用量子设备作为神经网络的第一层,探索了一种新型架构的可行性,事实证明这种架构有助于扩展 HNN 的训练过程。了解量子卷积操作的作用以及它们如何与经典神经网络相互作用,可以优化模型架构,使其在图像分类任务中更加高效和有效。本研究调查了 HNN 在不同数据集上的性能,包括 CIFAR100 和飓风破坏卫星图像,评估了 HNN 在这些数据集上的性能,并与参考经典模型的性能进行了比较。通过评估 HNN 在不同数据集上的可扩展性,该研究深入揭示了 HNN 在各种真实世界场景中的适用性,这对于构建适应不同环境的可持续机器学习解决方案至关重要。利用预训练模型(如 ResNet、EfficientNet 和 VGG16)的迁移学习技术,展示了 HNN 从经典神经网络的现有知识中获益的潜力。这种方法可以大大降低从头开始训练 HNN 的计算成本,同时还能获得具有竞争力的性能。本研究进行的可行性研究评估了在现实世界的图像分类任务中部署 HNN 的实用性和可行性。通过比较 HNN 与 ResNet、EfficientNet 和 VGG-16 等经典参考模型的性能,本研究证明了 HNN 在某些情况下的潜在优势。总之,通过提出新技术、优化模型架构并证明在现实世界的图像分类问题中采用 HNN 的可行性,本研究的发现有助于推进机器学习的可持续应用。这些见解有助于开发更高效、更环保的机器学习解决方案。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Multimodal Quanvolutional and Convolutional Neural Networks for Multi-Class Image Classification
By utilizing hybrid quantum–classical neural networks (HNNs), this research aims to enhance the efficiency of image classification tasks. HNNs allow us to utilize quantum computing to solve machine learning problems, which can be highly power-efficient and provide significant computation speedup compared to classical operations. This is particularly relevant in sustainable applications where reducing computational resources and energy consumption is crucial. This study explores the feasibility of a novel architecture by leveraging quantum devices as the first layer of the neural network, which proved to be useful for scaling HNNs’ training process. Understanding the role of quanvolutional operations and how they interact with classical neural networks can lead to optimized model architectures that are more efficient and effective for image classification tasks. This research investigates the performance of HNNs across different datasets, including CIFAR100 and Satellite Images of Hurricane Damage by evaluating the performance of HNNs on these datasets in comparison with the performance of reference classical models. By evaluating the scalability of HNNs on diverse datasets, the study provides insights into their applicability across various real-world scenarios, which is essential for building sustainable machine learning solutions that can adapt to different environments. Leveraging transfer learning techniques with pre-trained models such as ResNet, EfficientNet, and VGG16 demonstrates the potential for HNNs to benefit from existing knowledge in classical neural networks. This approach can significantly reduce the computational cost of training HNNs from scratch while still achieving competitive performance. The feasibility study conducted in this research assesses the practicality and viability of deploying HNNs for real-world image classification tasks. By comparing the performance of HNNs with classical reference models like ResNet, EfficientNet, and VGG-16, this study provides evidence of the potential advantages of HNNs in certain scenarios. Overall, the findings of this research contribute to advancing sustainable applications of machine learning by proposing novel techniques, optimizing model architectures, and demonstrating the feasibility of adopting HNNs for real-world image classification problems. These insights can inform the development of more efficient and environmentally friendly machine learning solutions.
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