mlsanityData to 7 Oct 2026

Generalised Implicit Neural Representations

arXiv 2205.15674 ↗First posted 31 May 2022cs.LG cs.AI eess.SPcited by 31

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Abstract (arXiv)

We consider the problem of learning implicit neural representations (INRs) for signals on non-Euclidean domains. In the Euclidean case, INRs are trained on a discrete sampling of a signal over a regular lattice. Here, we assume that the continuous signal exists on some unknown topological space from which we sample a discrete graph. In the absence of a coordinate system to identify the sampled nodes, we propose approximating their location with a spectral embedding of the graph. This allows us to train INRs without knowing the underlying continuous domain, which is the case for most graph signals in nature, while also making the INRs independent of any choice of coordinate system. We show experiments with our method on various real-world signals on non-Euclidean domains.

Key facts. Cited by 31 papers in the map (data through 7 Oct 2026). Builds on 30 of its 67 references that are in the map. First posted on arXiv 31 May 2022.

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30 of 67 references.

References · 30

  1. 2017Attention Is All You Needcited by 59,932
  2. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  3. 2019DeepSDF: Learning Continuous Signed Distance Functions for Shape Representationcited by 2,682
  4. 2020Implicit Neural Representations with Periodic Activation Functionscited by 2,214
  5. 2020Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domainscited by 2,170
  6. 2018Occupancy Networks: Learning 3D Reconstruction in Function Spacecited by 2,037
  7. 2016Geometric deep learning: going beyond Euclidean datacited by 1,042
  8. 2020Implicit Geometric Regularization for Learning Shapescited by 707
  9. 2020Benchmarking Graph Neural Networkscited by 672
  10. 2020A Generalization of Transformer Networks to Graphscited by 653
  11. 2019SAL: Sign Agnostic Learning of Shapes from Raw Datacited by 389
  12. 2021Graph Neural Networks with Learnable Structural and Positional Representationscited by 326
18 more
  1. 2017Community Detection and Stochastic Block Modelscited by 317
  2. 2021Vector Neurons: A General Framework for SO(3)-Equivariant Networkscited by 304
  3. 2019What graph neural networks cannot learn: depth vs widthcited by 195
  4. 2022Sign and Basis Invariant Networks for Spectral Graph Representation Learningcited by 176
  5. 2021GraphiT: Encoding Graph Structure in Transformerscited by 146
  6. 2020Adversarial Generation of Continuous Imagescited by 135
  7. 2020MeshSDF: Differentiable Iso-Surface Extractioncited by 112
  8. 20213D Neural Scene Representations for Visuomotor Controlcited by 110
  9. 2019Transferability of Spectral Graph Convolutional Neural Networkscited by 90
  10. 2021Beltrami Flow and Neural Diffusion on Graphscited by 84
  11. 2020Convergence and Stability of Graph Convolutional Networks on Large Random Graphscited by 75
  12. 2021Seeing Implicit Neural Representations as Fourier Seriescited by 50
  13. 2021Phase Transitions, Distance Functions, and Implicit Neural Representationscited by 38
  14. 2020Network Geometrycited by 31
  15. 2021On the Universality of Graph Neural Networks on Large Random Graphscited by 26
  16. 2022Intrinsic Neural Fields: Learning Functions on Manifoldscited by 20
  17. 2021A Unifying Generative Model for Graph Learning Algorithms: Label Propagation, Graph Convolutions, and Combinationscited by 18
  18. 2022Weisfeiler and Leman Go Infinite: Spectral and Combinatorial Pre-Coloringscited by 15

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Citing papers per year: 2023: 12; 2024: 12; 2025: 7.

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  1. 2023Geographic Location Encoding with Spherical Harmonics and Sinusoidal Representation Networkscited by 61
  2. 2023Swallowing the Bitter Pill: Simplified Scalable Conformer Generationcited by 55
  3. 2023Modality-Agnostic Variational Compression of Implicit Neural Representationscited by 22
  4. 2024Nonparametric Teaching of Implicit Neural Representationscited by 13
  5. 2023Interpretable statistical representations of neural population dynamics and geometrycited by 12
  6. 2023Manifold Diffusion Fieldscited by 11
  7. 2024Fast Training of Sinusoidal Neural Fields via Scaling Initializationcited by 11
  8. 2024Implicit Neural Representations for Robust Joint Sparse-View CT Reconstructioncited by 9
  9. 2023Implicit Neural Representations and the Algebra of Complex Waveletscited by 8
  10. 2024Imaging Interiors: An Implicit Solution to Electromagnetic Inverse Scattering Problemscited by 8
  11. 2023NAISR: A 3D Neural Additive Model for Interpretable Shape Representationcited by 7
  12. 2023In Search of a Data Transformation That Accelerates Neural Field Trainingcited by 7
19 more
  1. 2024Spatiotemporal Implicit Neural Representation as a Generalized Traffic Data Learnercited by 7
  2. 2023Implicit Gaussian process representation of vector fields over arbitrary latent manifoldscited by 6
  3. 2023Variational Mixture of HyperGenerators for Learning Distributions Over Functionscited by 5
  4. 2023Reconstructing Historical Climate Fields With Deep Learningcited by 4
  5. 2025Continuous Representation Methods, Theories, and Applications: An Overview and Perspectivescited by 4
  6. 2025Breaking the Discretization Barrier of Continuous Physics Simulation Learningcited by 4
  7. 2023SAIR: Learning Semantic-aware Implicit Representationcited by 3
  8. 2024Hybrid Neural Representations for Spherical Datacited by 3
  9. 2024NeuroGauss4D-PCI: 4D Neural Fields and Gaussian Deformation Fields for Point Cloud Interpolationcited by 3
  10. 2024MeshFeat: Multi-Resolution Features for Neural Fields on Meshescited by 3
  11. 2025NTK-Guided Implicit Neural Teachingcited by 2
  12. 2024Selective Test-Time Adaptation for Unsupervised Anomaly Detection using Neural Implicit Representationscited by 1
  13. 2024Continuous K-space Recovery Network with Image Guidance for Fast MRI Reconstructioncited by 1
  14. 2025Frequency-Aware Vision Transformers for High-Fidelity Super-Resolution of Earth System Modelscited by 1
  15. 2025Enhancing Robustness of Implicit Neural Representations Against Weight Perturbationscited by 1
  16. 2024MDNF: Multi-Diffusion-Nets for Neural Fields on Meshescited by 0
  17. 2024MING: A Functional Approach to Learning Molecular Generative Modelscited by 0
  18. 2025ViSIR: Vision Transformer Single Image Reconstruction Method for Earth System Modelscited by 0
  19. 2025CellINR: Implicitly Overcoming Photo-induced Artifacts in 4D Live Fluorescence Microscopycited by 0