知识蒸馏在遥感中的应用:调查

Yassine Himeur, Nour Aburaed, Omar Elharrouss, Iraklis Varlamis, Shadi Atalla, Wathiq Mansoor, Hussain Al Ahmad
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引用次数: 0

摘要

随着遥感(RS)领域模型的复杂性不断增加,人们对兼顾模型准确性和计算效率的解决方案的需求也越来越大。知识蒸馏(KD)是满足这一需求的有力工具,它能将知识从大型、复杂的模型转移到更小、更高效的模型中,而不会明显降低性能。这篇综述文章对 KD 及其在 RS 中的创新应用进行了广泛研究。KD 是一种将知识从复杂、繁琐的模型(教师)转移到更紧凑、更高效的模型(学生)的技术,在各个领域都有显著的发展和应用。首先,我们将介绍 KD 方法的基本概念和历史进程。文章强调了使用 KD 的优势,尤其是在模型压缩、提高计算效率和改善性能方面,这些优势对于 RS 场景中的实际部署至关重要。文章对 KD 技术进行了全面分类,对每一类技术都进行了批判性分析,以展示备选方案的广度和深度,并通过具体案例研究展示了 KD 方法在 RS 任务(如实例分割和对象检测)中的实际应用。此外,综述还讨论了 KD 在 RS 中面临的挑战和局限性,包括实际限制和未来发展方向,为 RS 领域的研究人员和从业人员提供了一个全面的视角。通过这样的组织,本文不仅阐明了 KD 的研究现状,还为未来的研究机会奠定了基础,从而为学术研究和实际应用做出了重要贡献。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Applications of Knowledge Distillation in Remote Sensing: A Survey
With the ever-growing complexity of models in the field of remote sensing (RS), there is an increasing demand for solutions that balance model accuracy with computational efficiency. Knowledge distillation (KD) has emerged as a powerful tool to meet this need, enabling the transfer of knowledge from large, complex models to smaller, more efficient ones without significant loss in performance. This review article provides an extensive examination of KD and its innovative applications in RS. KD, a technique developed to transfer knowledge from a complex, often cumbersome model (teacher) to a more compact and efficient model (student), has seen significant evolution and application across various domains. Initially, we introduce the fundamental concepts and historical progression of KD methods. The advantages of employing KD are highlighted, particularly in terms of model compression, enhanced computational efficiency, and improved performance, which are pivotal for practical deployments in RS scenarios. The article provides a comprehensive taxonomy of KD techniques, where each category is critically analyzed to demonstrate the breadth and depth of the alternative options, and illustrates specific case studies that showcase the practical implementation of KD methods in RS tasks, such as instance segmentation and object detection. Further, the review discusses the challenges and limitations of KD in RS, including practical constraints and prospective future directions, providing a comprehensive overview for researchers and practitioners in the field of RS. Through this organization, the paper not only elucidates the current state of research in KD but also sets the stage for future research opportunities, thereby contributing significantly to both academic research and real-world applications.
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