1 citations · 1 across the 4 of their papers we have counts for
4 papers
Geometry Preserving Loss Functions Promote Improved Adaptation of Blackbox Generative Model
Sinjini Mitra, Constantine Kyriakakis, Shenyuan Liang +2
Adaptation of blackbox generative models has been widely studied recently through the exploration of several methods including generator fine-tuning, latent space searches, leverag…
Investigating Image Manifolds of 3D Objects: Learning, Shape Analysis, and Comparisons
Benjamin Beaudett, Shenyuan Liang, Anuj Srivastava
Despite high-dimensionality of images, the sets of images of 3D objects have long been hypothesized to form low-dimensional manifolds. What is the nature of such manifolds? How do…
Shape-Graph Matching Network (SGM-net): Registration for Statistical Shape Analysis
Shenyuan Liang, Mauricio Pamplona Segundo, Sathyanarayanan N. Aakur +2
This paper focuses on the statistical analysis of shapes of data objects called shape graphs, a set of nodes connected by articulated curves with arbitrary shapes. A critical need…
Learning Pose Image Manifolds Using Geometry-Preserving GANs and Elasticae
Shenyuan Liang, Pavan Turaga, Anuj Srivastava
This paper investigates the challenge of learning image manifolds, specifically pose manifolds, of 3D objects using limited training data. It proposes a DNN approach to manifold le…