Where Do We Stand with Implicit Neural Representations? A Technical and Performance Survey

2024-11-06

arXiv.org

AI Summary

This paper systematically surveys implicit neural representations (INRs), identifying key performance bottlenecks—particularly limited expressivity and scalability—in inverse problems such as audio synthesis, image reconstruction, 3D scene modeling, and high-dimensional data generation. To address these challenges, we propose the first unified four-dimensional taxonomy—spanning activation functions, positional encodings, joint encoding strategies, and network architectures—and uncover a fundamental trade-off between local bias suppression and fine-grained detail modeling. Through cross-modal benchmarking and gradient-guided structural optimization, we quantitatively evaluate reconstruction fidelity, memory efficiency, and generalization capability. Our contributions include: (i) an open, reproducible experimental benchmark; (ii) identification of three critical research frontiers—activation expressivity, positional encoding robustness, and high-dimensional scalability; and (iii) theoretically grounded, practice-oriented guidance for INR method selection and future development.

Technology Category

• Machine Learning: Representation Learning
• Computer Vision: Representation Learning for Vision
• Natural Language Processing: Generation

Application Category

• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding
• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

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.

Problem

• Evaluates Implicit Neural Representations
• Analyzes INR methods taxonomy
• Identifies INR limitations improvements

Innovation

• Utilizes multilayer perceptrons (MLPs)
• Models data as continuous functions
• Categorizes methods into four key areas