Implicit Neural Representations (INRs) have emerged as a new paradigm for representing signals and scenes, demonstrating flexibility and strong performance across diverse applications. INRs model data as continuous implicit functions with multilayer perceptrons (MLPs), offering advantages such as resolution independence and memory efficiency beyond discretized data structures. This survey provides a comprehensive review of recent INR methodologies and introduces a taxonomy that categorizes existing approaches into six groups: (1) activation functions, (2) positional encoding, (3) fourier-based reparameterization, (4) combined strategies, (5) implicit neural conditioning with prior knowledge, and (6) function decomposition with learnable operators. We analyze the core properties of INR models, highlighting their differentiability, compactness, and adaptability to varying resolutions, and discuss open challenges such as spectral bias and limitations in modeling high-frequency signals. Furthermore, we conduct a comparative analysis across representative methods to clarify similarities, differences, and design trade-offs. Through this synthesis, we aim to provide an accessible and structured reference that not only outlines the current landscape of INR research but also identifies gaps and opportunities for future exploration and advancement.
Ko et al. (Tue,) studied this question.