Work

SrvfNet: A Generative Network for Unsupervised Multiple Diffeomorphic Functional Alignment

Year: 2021

Type: conference-paper

Abstract: We present SrvfNet, a generative deep learning framework for the joint multiple alignment of large collections of functional data comprising square-root velocity functions (SRVF) to their templates. Our proposed framework is fully unsupervised and is capable of aligning to a predefined template as well as jointly predicting an optimal template from data while simultaneously achieving alignment. Our network is constructed as a generative encoder-decoder architecture comprising fully-connected layers capable of producing a distribution space of the warping functions. We demonstrate the strength of our framework by validating it on synthetic data as well as diffusion profiles from magnetic resonance imaging (MRI) data.

Source: IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

DOI: 10.1109/cvprw53098.2021.00505

Authors: Elvis Nunez, Andrew Lizarraga, Shantanu H. Joshi

Institution: University of California, Los Angeles

Language: English

Topic: Topological and Geometric Data Analysis

Subfield: Computational Theory and Mathematics

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funder: National Institute on Alcohol Abuse and Alcoholism

Awards: Longitudinal mapping of brain-face-behavior trajectories in prenatal alcohol exposure from birth through adolescence, Brain and Cognitive Development in the PASS Cohort: The Impact of PrenatalAlcohol Exposure