20 citations · 29 across the 4 of their papers we have counts for
8 papers
Near Perfect GAN Inversion
Qianli Feng, Viraj Shah, Raghudeep Gadde +2
To edit a real photo using Generative Adversarial Networks (GANs), we need a GAN inversion algorithm to identify the latent vector that perfectly reproduces it. Unfortunately, wher…
Provably Convergent Algorithms for Solving Inverse Problems Using Generative Models
Viraj Shah, Rakib Hyder, M. Salman Asif +1
The traditional approach of hand-crafting priors (such as sparsity) for solving inverse problems is slowly being replaced by the use of richer learned priors (such as those modeled…
Encoding Invariances in Deep Generative Models
Viraj Shah, Ameya Joshi, Sambuddha Ghosal +4
Reliable training of generative adversarial networks (GANs) typically require massive datasets in order to model complicated distributions. However, in several applications, traini…
Alternating Phase Projected Gradient Descent with Generative Priors for Solving Compressive Phase Retrieval
Rakib Hyder, Viraj Shah, Chinmay Hegde +1
The classical problem of phase retrieval arises in various signal acquisition systems. Due to the ill-posed nature of the problem, the solution requires assumptions on the structur…
Signal Reconstruction from Modulo Observations
Viraj Shah, Chinmay Hegde
We consider the problem of reconstructing a signal from under-determined modulo observations (or measurements). This observation model is inspired by a (relatively) less well-known…
Physics-aware Deep Generative Models for Creating Synthetic Microstructures
Rahul Singh, Viraj Shah, Balaji Pokuri +3
A key problem in computational material science deals with understanding the effect of material distribution (i.e., microstructure) on material performance. The challenge is to syn…