Reconstruction of Fields from Sparse Sensing: Differentiable Sensor Placement Enhances Generalization

• URL: http://arxiv.org/abs/2312.09176v1
• Date: Thu, 14 Dec 2023 17:44:09 GMT
• Title: Reconstruction of Fields from Sparse Sensing: Differentiable Sensor Placement Enhances Generalization
• Authors: Agnese Marcato, Daniel O'Malley, Hari Viswanathan, Eric Guiltinan, Javier E. Santos
• Abstract summary: We introduce a general approach that employs differentiable programming to exploit sensor placement within the training of a neural network model. Our method of differentiable placement strategies has the potential to significantly increase data collection efficiency, enable more thorough area coverage, and reduce redundancy in sensor deployment.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recreating complex, high-dimensional global fields from limited data points is a grand challenge across various scientific and industrial domains. Given the prohibitive costs of specialized sensors and the frequent inaccessibility of certain regions of the domain, achieving full field coverage is typically not feasible. Therefore, the development of algorithms that intelligently improve sensor placement is of significant value. In this study, we introduce a general approach that employs differentiable programming to exploit sensor placement within the training of a neural network model in order to improve field reconstruction. We evaluated our method using two distinct datasets; the results show that our approach improved test scores. Ultimately, our method of differentiable placement strategies has the potential to significantly increase data collection efficiency, enable more thorough area coverage, and reduce redundancy in sensor deployment.

Related papers

• Disentangling Masked Autoencoders for Unsupervised Domain Generalization [57.56744870106124]
  Unsupervised domain generalization is fast gaining attention but is still far from well-studied. Disentangled Masked Auto (DisMAE) aims to discover the disentangled representations that faithfully reveal intrinsic features. DisMAE co-trains the asymmetric dual-branch architecture with semantic and lightweight variation encoders.
  arXiv  Detail & Related papers  (2024-07-10T11:11:36Z)
• DiffuBox: Refining 3D Object Detection with Point Diffusion [74.01759893280774]
  We introduce a novel diffusion-based box refinement approach to ensure robust 3D object detection and localization. We evaluate this approach under various domain adaptation settings, and our results reveal significant improvements across different datasets.
  arXiv  Detail & Related papers  (2024-05-25T03:14:55Z)
• DynST: Dynamic Sparse Training for Resource-Constrained Spatio-Temporal Forecasting [24.00162014044092]
  Earth science systems rely heavily on the extensive deployment of sensors. Traditional approaches to sensor deployment utilize specific algorithms to design and deploy sensors. In this paper, we introduce for the first time the concept of dynamic sparse training and are committed to adaptively, dynamically filtering important sensor data.
  arXiv  Detail & Related papers  (2024-03-05T12:31:24Z)
• Improving Intrusion Detection with Domain-Invariant Representation Learning in Latent Space [4.871119861180455]
  We introduce a two-phase representation learning technique using multi-task learning. We disentangle the latent space by minimizing the mutual information between the prior and latent space. We assess the model's efficacy across multiple cybersecurity datasets.
  arXiv  Detail & Related papers  (2023-12-28T17:24:13Z)
• Explore-Instruct: Enhancing Domain-Specific Instruction Coverage through Active Exploration [64.58185031596169]
  Explore-Instruct is a novel approach to enhance the data coverage to be used in domain-specific instruction-tuning. Our data-centric analysis validates the effectiveness of this proposed approach in improving domain-specific instruction coverage. Our findings offer a promising opportunity to improve instruction coverage, especially in domain-specific contexts.
  arXiv  Detail & Related papers  (2023-10-13T15:03:15Z)
• Efficient Sensor Placement from Regression with Sparse Gaussian Processes in Continuous and Discrete Spaces [4.40560654491339]
  The sensor placement problem is a common problem that arises when monitoring correlated phenomena. We have uncovered a novel connection between the sensor placement problem and sparse processes(SGP) Our approach generates sensor placements consistently on par with or better than the prior state-of-the-art approaches.
  arXiv  Detail & Related papers  (2023-02-28T19:10:12Z)
• Evaluating the Label Efficiency of Contrastive Self-Supervised Learning for Multi-Resolution Satellite Imagery [0.0]
  Self-supervised learning has been applied in the remote sensing domain to exploit readily-available unlabeled data. In this paper, we study self-supervised visual representation learning through the lens of label efficiency.
  arXiv  Detail & Related papers  (2022-10-13T06:54:13Z)
• Coarse to Fine: Domain Adaptive Crowd Counting via Adversarial Scoring Network [58.05473757538834]
  This paper proposes a novel adversarial scoring network (ASNet) to bridge the gap across domains from coarse to fine granularity. Three sets of migration experiments show that the proposed methods achieve state-of-the-art counting performance.
  arXiv  Detail & Related papers  (2021-07-27T14:47:24Z)
• Regressive Domain Adaptation for Unsupervised Keypoint Detection [67.2950306888855]
  Domain adaptation (DA) aims at transferring knowledge from a labeled source domain to an unlabeled target domain. We present a method of regressive domain adaptation (RegDA) for unsupervised keypoint detection. Our method brings large improvement by 8% to 11% in terms of PCK on different datasets.
  arXiv  Detail & Related papers  (2021-03-10T16:45:22Z)
• Global field reconstruction from sparse sensors with Voronoi tessellation-assisted deep learning [0.0]
  We propose a data-driven spatial field recovery technique based on a structured grid-based deep-learning approach for arbitrary positioned sensors of any numbers. The presented technique opens a new pathway towards the practical use of neural networks for real-time global field estimation.
  arXiv  Detail & Related papers  (2021-01-03T03:43:53Z)
• Cross-Domain Facial Expression Recognition: A Unified Evaluation Benchmark and Adversarial Graph Learning [85.6386289476598]
  We develop a novel adversarial graph representation adaptation (AGRA) framework for cross-domain holistic-local feature co-adaptation. We conduct extensive and fair evaluations on several popular benchmarks and show that the proposed AGRA framework outperforms previous state-of-the-art methods.
  arXiv  Detail & Related papers  (2020-08-03T15:00:31Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.