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Hybrid Neural Representations for Spherical Data

Published 5 Feb 2024 in cs.LG and eess.SP | (2402.05965v1)

Abstract: In this paper, we study hybrid neural representations for spherical data, a domain of increasing relevance in scientific research. In particular, our work focuses on weather and climate data as well as comic microwave background (CMB) data. Although previous studies have delved into coordinate-based neural representations for spherical signals, they often fail to capture the intricate details of highly nonlinear signals. To address this limitation, we introduce a novel approach named Hybrid Neural Representations for Spherical data (HNeR-S). Our main idea is to use spherical feature-grids to obtain positional features which are combined with a multilayer perception to predict the target signal. We consider feature-grids with equirectangular and hierarchical equal area isolatitude pixelization structures that align with weather data and CMB data, respectively. We extensively verify the effectiveness of our HNeR-S for regression, super-resolution, temporal interpolation, and compression tasks.

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References (51)
  1. Planck 2013 results. i. overview of products and scientific results. Astronomy & Astrophysics, 571:A1, 2014.
  2. Agarap, A. F. Deep learning using rectified linear units (relu). arXiv preprint arXiv:1803.08375, 2018.
  3. Four-year cobe* dmr cosmic microwave background observations: maps and basic results. The Astrophysical Journal, 464(1):L1, 1996.
  4. Pangu-weather: A 3d high-resolution model for fast and accurate global weather forecast. arXiv preprint arXiv:2211.02556, 2022.
  5. Spherical fourier neural operators: Learning stable dynamics on the sphere. In International Conference on Learning Representations, 2023. URL https://openreview.net/forum?id=TwsJ9IOZDx.
  6. Fostering the development of climate services through copernicus climate change service (c3s) for agriculture applications. Weather and Climate Extremes, 27:100226, 2020.
  7. Deepcmb: Lensing reconstruction of the cosmic microwave background with deep neural networks. Astronomy and Computing, 28:100307, 2019.
  8. Cenn: A fully convolutional neural network for cmb recovery in realistic microwave sky simulations. arXiv preprint arXiv:2205.05623, 2022.
  9. Deep local shapes: Learning local sdf priors for detailed 3d reconstruction. In Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16, pp.  608–625. Springer, 2020.
  10. Learning continuous image representation with local implicit image function. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp.  8628–8638, 2021.
  11. Spherical CNNs. In International Conference on Learning Representations, 2018. URL https://openreview.net/forum?id=Hkbd5xZRb.
  12. A flat universe from high-resolution maps of the cosmic microwave background radiation. Nature, 404(6781):955–959, 2000.
  13. Deepsphere: a graph-based spherical cnn. arXiv preprint arXiv:2012.15000, 2020.
  14. Multidetector multicomponent spectral matching and applications for cosmic microwave background data analysis. Monthly Notices of the Royal Astronomical Society, 346(4):1089–1102, 2003.
  15. Coin++: Neural compression across modalities. arXiv preprint arXiv:2201.12904, 2022.
  16. Generalized fourier features for coordinate-based learning of functions on manifolds, 2022. URL https://openreview.net/forum?id=g6UqpVislvH.
  17. Healpix: A framework for high-resolution discretization and fast analysis of data distributed on the sphere. The Astrophysical Journal, 622(2):759, 2005.
  18. Generalised implicit neural representations. Advances in Neural Information Processing Systems, 35:30446–30458, 2022.
  19. Era5 hourly data on single levels from 1979 to present. Copernicus climate change service (c3s) climate data store (cds), 10(10.24381), 2018.
  20. Parameter estimation for the cosmic microwave background with bayesian neural networks. Physical Review D, 102(10):103509, 2020.
  21. Compressing multidimensional weather and climate data into neural networks. arXiv preprint arXiv:2210.12538, 2022.
  22. Compressing multidimensional weather and climate data into neural networks. In The Eleventh International Conference on Learning Representations, 2023. URL https://openreview.net/forum?id=Y5SEe3dfniJ.
  23. Neural tangent kernel: Convergence and generalization in neural networks. Advances in neural information processing systems, 31, 2018.
  24. C3: High-performance and low-complexity neural compression from a single image or video. arXiv preprint arXiv:2312.02753, 2023.
  25. Intrinsic neural fields: Learning functions on manifolds. In European Conference on Computer Vision, pp.  622–639. Springer, 2022.
  26. Convolutional neural networks on the healpix sphere: a pixel-based algorithm and its application to cmb data analysis. Astronomy & Astrophysics, 628:A129, 2019.
  27. Cool-chic: Coordinate-based low complexity hierarchical image codec. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp.  13515–13522, 2023.
  28. Graphcast: Learning skillful medium-range global weather forecasting. arXiv preprint arXiv:2212.12794, 2022.
  29. Sz3: A modular framework for composing prediction-based error-bounded lossy compressors. IEEE Transactions on Big Data, 9(2):485–498, 2022.
  30. Neural sparse voxel fields. Advances in Neural Information Processing Systems, 33:15651–15663, 2020.
  31. Decoupled weight decay regularization. arXiv preprint arXiv:1711.05101, 2017.
  32. Acorn: Adaptive coordinate networks for neural scene representation. arXiv preprint arXiv:2105.02788, 2021.
  33. Multiresolution healpix maps for multiwavelength and multimessenger astronomy. The Astronomical Journal, 163(6):259, 2022.
  34. Nerf: Representing scenes as neural radiance fields for view synthesis. Communications of the ACM, 65(1):99–106, 2021.
  35. Inpainting cmb maps using partial convolutional neural networks. Journal of Cosmology and Astroparticle Physics, 2021(03):055, 2021.
  36. Instant neural graphics primitives with a multiresolution hash encoding. ACM Transactions on Graphics (ToG), 41(4):1–15, 2022.
  37. Deepsdf: Learning continuous signed distance functions for shape representation. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp.  165–174, 2019.
  38. Neural body: Implicit neural representations with structured latent codes for novel view synthesis of dynamic humans. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp.  9054–9063, 2021.
  39. On the spectral bias of neural networks. In International Conference on Machine Learning, pp.  5301–5310. PMLR, 2019.
  40. Wire: Wavelet implicit neural representations. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp.  18507–18516, 2023.
  41. Modality-agnostic variational compression of implicit neural representations. arXiv preprint arXiv:2301.09479, 2023.
  42. Implicit neural representations with periodic activation functions. Advances in neural information processing systems, 33:7462–7473, 2020.
  43. First-year wilkinson microwave anisotropy probe (wmap)* observations: determination of cosmological parameters. The Astrophysical Journal Supplement Series, 148(1):175, 2003.
  44. Adversarial super-resolution of climatological wind and solar data. Proceedings of the National Academy of Sciences, 117(29):16805–16815, 2020.
  45. Fourier features let networks learn high frequency functions in low dimensional domains. Advances in Neural Information Processing Systems, 33:7537–7547, 2020.
  46. Recovering the cmb signal with machine learning. The Astrophysical Journal Supplement Series, 260(1):13, 2022.
  47. Fast and accurate learned multiresolution dynamical downscaling for precipitation. Geoscientific Model Development, 14(10):6355–6372, 2021.
  48. Esrgan: Enhanced super-resolution generative adversarial networks. In Proceedings of the European conference on computer vision (ECCV) workshops, pp.  0–0, 2018.
  49. Neural fields in visual computing and beyond. arXiv preprint 2111.11426, 2021.
  50. Statistical downscaling of numerical weather prediction based on convolutional neural networks. Global Energy Interconnection, 5(2):217–225, 2022.
  51. Stable machine-learning parameterization of subgrid processes for climate modeling at a range of resolutions. Nature communications, 11(1):3295, 2020.
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