2000 character limit reached
S3RP: Self-Supervised Super-Resolution and Prediction for Advection-Diffusion Process (2111.04639v1)
Published 8 Nov 2021 in cs.LG, cs.CV, and physics.comp-ph
Abstract: We present a super-resolution model for an advection-diffusion process with limited information. While most of the super-resolution models assume high-resolution (HR) ground-truth data in the training, in many cases such HR dataset is not readily accessible. Here, we show that a Recurrent Convolutional Network trained with physics-based regularizations is able to reconstruct the HR information without having the HR ground-truth data. Moreover, considering the ill-posed nature of a super-resolution problem, we employ the Recurrent Wasserstein Autoencoder to model the uncertainty.
- Image super-resolution using deep convolutional networks. IEEE transactions on pattern analysis and machine intelligence, 38(2):295–307, 2015.
- Esrgan: Enhanced super-resolution generative adversarial networks. In Proceedings of the European conference on computer vision (ECCV) workshops, pages 0–0, 2018.
- Srflow: Learning the super-resolution space with normalizing flow. In European Conference on Computer Vision, pages 715–732. Springer, 2020.
- Variational inference with normalizing flows. In International conference on machine learning, pages 1530–1538. PMLR, 2015.
- Pulse: Self-supervised photo upsampling via latent space exploration of generative models. In Proceedings of the ieee/cvf conference on computer vision and pattern recognition, pages 2437–2445, 2020.
- A style-based generator architecture for generative adversarial networks. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 4401–4410, 2019.
- Mocogan: Decomposing motion and content for video generation. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 1526–1535, 2018.
- S3vae: Self-supervised sequential vae for representation disentanglement and data generation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 6538–6547, 2020.
- Videoflow: A conditional flow-based model for stochastic video generation. arXiv preprint arXiv:1903.01434, 2019.
- Deepsd: Generating high resolution climate change projections through single image super-resolution. In Proceedings of the 23rd acm sigkdd international conference on knowledge discovery and data mining, pages 1663–1672, 2017.
- Physics-informed neural network super resolution for advection-diffusion models. arXiv preprint arXiv:2011.02519, 2020.
- Super-resolution and denoising of fluid flow using physics-informed convolutional neural networks without high-resolution labels. Physics of Fluids, 33(7):073603, 2021.
- Disentangling physical dynamics from unknown factors for unsupervised video prediction. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 11474–11484, 2020.
- Metnet: A neural weather model for precipitation forecasting. arXiv preprint arXiv:2003.12140, 2020.
- Towards physics-informed deep learning for turbulent flow prediction. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pages 1457–1466, 2020.
- Meshfreeflownet: a physics-constrained deep continuous space-time super-resolution framework. In SC20: International Conference for High Performance Computing, Networking, Storage and Analysis, pages 1–15. IEEE, 2020.
- tempogan: A temporally coherent, volumetric gan for super-resolution fluid flow. ACM Transactions on Graphics (TOG), 37(4):1–15, 2018.
- Development of hpℎ𝑝hpitalic_h italic_p-inverse model by using generalized polynomial chaos. Computer Methods in Applied Mechanics and Engineering, 347:1–20, 2019.
- Disentangled recurrent wasserstein autoencoder. arXiv preprint arXiv:2101.07496, 2021.
- Training generative neural networks via maximum mean discrepancy optimization. arXiv preprint arXiv:1505.03906, 2015.
Paper Prompts
Sign up for free to create and run prompts on this paper using GPT-5.
Top Community Prompts
Collections
Sign up for free to add this paper to one or more collections.