58 citations · 79 across the 8 of their papers we have counts for
8 papers
A dimension-reduced variational approach for solving physics-based inverse problems using generative adversarial network priors and normalizing flows
Agnimitra Dasgupta, Dhruv V Patel, Deep Ray +2
We propose a novel modular inference approach combining two different generative models -- generative adversarial networks (GAN) and normalizing flows -- to approximate the posteri…
SRTGAN: Triplet Loss based Generative Adversarial Network for Real-World Super-Resolution
Dhruv Patel, Abhinav Jain, Simran Bawkar +6
Many applications such as forensics, surveillance, satellite imaging, medical imaging, etc., demand High-Resolution (HR) images. However, obtaining an HR image is not always possib…
GDIP: Gated Differentiable Image Processing for Object-Detection in Adverse Conditions
Sanket Kalwar, Dhruv Patel, Aakash Aanegola +3
Detecting objects under adverse weather and lighting conditions is crucial for the safe and continuous operation of an autonomous vehicle, and remains an unsolved problem. We prese…
Variationally Mimetic Operator Networks
Dhruv Patel, Deep Ray, Michael R. A. Abdelmalik +2
In recent years operator networks have emerged as promising deep learning tools for approximating the solution to partial differential equations (PDEs). These networks map input fu…
The efficacy and generalizability of conditional GANs for posterior inference in physics-based inverse problems
Deep Ray, Harisankar Ramaswamy, Dhruv V. Patel +1
In this work, we train conditional Wasserstein generative adversarial networks to effectively sample from the posterior of physics-based Bayesian inference problems. The generator…
Solution of Physics-based Bayesian Inverse Problems with Deep Generative Priors
Dhruv V Patel, Deep Ray, Assad A Oberai
Inverse problems are ubiquitous in nature, arising in almost all areas of science and engineering ranging from geophysics and climate science to astrophysics and biomechanics. One…
GAN-based Priors for Quantifying Uncertainty
Dhruv V. Patel, Assad A. Oberai
Bayesian inference is used extensively to quantify the uncertainty in an inferred field given the measurement of a related field when the two are linked by a mathematical model. De…
Bayesian Inference with Generative Adversarial Network Priors
Dhruv Patel, Assad A Oberai
Bayesian inference is used extensively to infer and to quantify the uncertainty in a field of interest from a measurement of a related field when the two are linked by a physical m…