20 citations · 71 across the 12 of their papers we have counts for
13 papers
FMS: Unified Flow Matching for Segmentation and Synthesis of Thin Structures
Babak Asadi, Peiyang Wu, Mani Golparvar-Fard +2
Segmenting thin structures like infrastructure cracks and anatomical vessels is a task hampered by topology-sensitive geometry, high annotation costs, and poor generalization acros…
JoIN: Joint GANs Inversion for Intrinsic Image Decomposition
Viraj Shah, Svetlana Lazebnik, Julien Philip
Intrinsic Image Decomposition (IID) is a challenging inverse problem that seeks to decompose a natural image into its underlying intrinsic components such as albedo and shading. Wh…
Make It So: Steering StyleGAN for Any Image Inversion and Editing
Anand Bhattad, Viraj Shah, Derek Hoiem +1
StyleGAN's disentangled style representation enables powerful image editing by manipulating the latent variables, but accurately mapping real-world images to their latent variables…
MultiStyleGAN: Multiple One-shot Image Stylizations using a Single GAN
Viraj Shah, Ayush Sarkar, Sudharsan Krishnakumar Anitha +1
Image stylization aims at applying a reference style to arbitrary input images. A common scenario is one-shot stylization, where only one example is available for each reference st…
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…
CloudFindr: A Deep Learning Cloud Artifact Masker for Satellite DEM Data
Kalina Borkiewicz, Viraj Shah, J. P. Naiman +3
Artifact removal is an integral component of cinematic scientific visualization, and is especially challenging with big datasets in which artifacts are difficult to define. In this…
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…
Solving Linear Inverse Problems Using GAN Priors: An Algorithm with Provable Guarantees
Viraj Shah, Chinmay Hegde
In recent works, both sparsity-based methods as well as learning-based methods have proven to be successful in solving several challenging linear inverse problems. However, sparsit…
Reconstruction from Periodic Nonlinearities, With Applications to HDR Imaging
Viraj Shah, Mohammadreza Soltani, Chinmay Hegde
We consider the problem of reconstructing signals and images from periodic nonlinearities. For such problems, we design a measurement scheme that supports efficient reconstruction;…