p$^3$VAE: a physics-integrated generative model. Application to the pixel-wise classification of airborne hyperspectral images
The combination of machine learning models with physical models is a recent research path to learn robust data representations. In this paper, we introduce p$^3$VAE, a generative model that integrates a physical model which deterministically models some of the true underlying factors of variation in the data. To fully leverage our hybrid design, we enhance an existing semi-supervised optimization technique and introduce a new inference scheme that comes along meaningful uncertainty estimates. We apply p$^3$VAE to the pixel-wise classification of airborne hyperspectral images. Our experiments on simulated and real data demonstrate the benefits of our hybrid model against conventional machine learning models in terms of extrapolation capabilities and interpretability. In particular, we show that p$^3$VAE naturally has high disentanglement capabilities. Our code and data have been made publicly available at https://github.com/Romain3Ch216/p3VAE.
Code (1)
Tasks
DisentanglementSemantic SegmentationSimilar Papers 제목 키워드 기반
Physics-driven generative adversarial networks empower single-pixel infrared hyperspectral imaging
A physics-driven generative adversarial network (GAN) was established here for single-pixel hyperspectral imaging (HSI) in the infrared spectrum, to eliminate the extensive data training work required by traditional data…
compressed sensingGenerative Adversarial NetworkA 3D super-resolution of wind fields via physics-informed pixel-wise self-attention generative adversarial network
To mitigate global warming, greenhouse gas sources need to be resolved at a high spatial resolution and monitored in time to ensure the reduction and ultimately elimination of the pollution source. However, the complexit…
Generative Adversarial NetworkSuper-ResolutionGroup Anomaly Detection using Deep Generative Models
Unlike conventional anomaly detection research that focuses on point anomalies, our goal is to detect anomalous collections of individual data points. In particular, we perform group anomaly detection (GAD) with an empha…
Anomaly DetectionGroup Anomaly DetectionDynamic Diffusion Schrödinger Bridge in Astrophysical Observational Inversions
We study Diffusion Schr\"odinger Bridge (DSB) models in the context of dynamical astrophysical systems, specifically tackling observational inverse prediction tasks within Giant Molecular Clouds (GMCs) for star formation…
Physical SimulationsPredictionPhysics-integrated generative modeling using attentive planar normalizing flow based variational autoencoder
Physics-integrated generative modeling is a class of hybrid or grey-box modeling in which we augment the the data-driven model with the physics knowledge governing the data distribution. The use of physics knowledge allo…