Implicit Neural Representations (INRs) have emerged as a paradigm in knowledge representation, offering exceptional flexibility and performance across a diverse range of applications. INRs leverage multilayer perceptrons (MLPs) to model data as continuous implicit functions, providing critical advantages such as resolution independence, memory efficiency, and generalisation beyond discretised data structures. Their ability to solve complex inverse problems makes them particularly effective for tasks including audio reconstruction, image representation, 3D object reconstruction, and high-dimensional data synthesis. This survey provides a comprehensive review of state-of-the-art INR methods, introducing a clear taxonomy that categorises them into four key areas: activation functions, position encoding, combined strategies, and network structure optimisation. We rigorously analyse their critical properties, such as full differentiability, smoothness, compactness, and adaptability to varying resolutions while also examining their strengths and limitations in addressing locality biases and capturing fine details. Our experimental comparison offers new insights into the trade-offs between different approaches, showcasing the capabilities and challenges of the latest INR techniques across various tasks. In addition to identifying areas where current methods excel, we highlight key limitations and potential avenues for improvement, such as developing more expressive activation functions, enhancing positional encoding mechanisms, and improving scalability for complex, high-dimensional data. This survey serves as a roadmap for researchers, offering practical guidance for future exploration in the field of INRs. We aim to foster new methodologies by outlining promising research directions for INRs and applications.

翻译：隐式神经表示（INRs）已成为知识表示领域的一种新兴范式，在多样化应用中展现出卓越的灵活性与性能。INRs利用多层感知机（MLPs）将数据建模为连续隐函数，具备分辨率无关性、内存高效性以及超越离散数据结构的泛化能力等关键优势。其解决复杂逆问题的能力使其在音频重建、图像表示、三维物体重建和高维数据合成等任务中表现尤为突出。本综述系统回顾了前沿INR方法，提出清晰的分类体系，将其归纳为四个关键方向：激活函数、位置编码、组合策略及网络结构优化。我们深入分析了其关键特性，包括完全可微性、平滑性、紧凑性和多分辨率适应性，同时考察了其在处理局部性偏差与捕捉精细细节方面的优势与局限。实验对比研究揭示了不同方法间的权衡关系，展现了最新INR技术在不同任务中的能力与挑战。除总结现有方法的优势领域外，我们重点指出了关键局限性及改进方向，例如开发更具表达力的激活函数、增强位置编码机制、提升复杂高维数据的可扩展性等。本综述为研究者提供了路线图，为INR领域的未来探索提供实践指导。通过勾勒INRs及其应用的潜在研究方向，我们旨在推动新方法论的创新发展。