NDLI: Model Transport: Towards Scalable Transfer Learning on Manifolds

Content Provider	IEEE Xplore Digital Library
Author	Freifeld, O. Hauberg, S. Black, M.J.
Copyright Year	2014
Description	Author affiliation: DTU Compute, Lyngby, Denmark (Hauberg, S.) \|\| MIT, Cambridge, MA, USA (Freifeld, O.) \|\| MPI for Intell. Syst., Tϋbingen, Germany (Black, M.J.)
Abstract	We consider the intersection of two research fields: transfer learning and statistics on manifolds. In particular, we consider, for manifold-valued data, transfer learning of tangent-space models such as Gaussians distributions, PCA, regression, or classifiers. Though one would hope to simply use ordinary $R^{n}-transfer$ learning ideas, the manifold structure prevents it. We overcome this by basing our method on inner-product-preserving parallel transport, a well-known tool widely used in other problems of statistics on manifolds in computer vision. At first, this straight-forward idea seems to suffer from an obvious shortcoming: Transporting large datasets is prohibitively expensive, hindering scalability. Fortunately, with our approach, we never transport data. Rather, we show how the statistical models themselves can be transported, and prove that for the tangent-space models above, the transport "commutes" with learning. Consequently, our compact framework, ap- plicable to a large class of manifolds, is not restricted by the size of either the training or test sets. We demonstrate the approach by transferring PCA and logistic-regression models of real-world data involving 3D shapes and image descriptors.
Starting Page	1378
Ending Page	1385
File Size	1455474
Page Count	8
File Format	PDF
ISBN	9781479951185
ISSN	10636919
DOI	10.1109/CVPR.2014.179
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-06-23
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Manifolds Computational modeling Total quality management Principal component analysis Data models Shape Vectors PGA Statistics on Manifolds Computer Vision Transfer Learning Scalable Riemannian Manifolds Manifold-Valued Data
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Riemannian Manifold Learning

Curvature-Aware Regularization on Riemannian Submanifolds

Learning a Manifold as an Atlas

Grassmann Averages for Scalable Robust PCA

A semi-supervised framework for mapping data to the intrinsic manifold

Inductive Hashing on Manifolds

Model Transport: Towards Scalable Transfer Learning on Manifolds

Towards Scalable Transfer Learning on Manifolds (2014)

On the Quotient Representation for the Essential Manifold

Model Transport: Towards Scalable Transfer Learning on Manifolds

Similar Documents

Riemannian Manifold Learning

Curvature-Aware Regularization on Riemannian Submanifolds

Learning a Manifold as an Atlas

Grassmann Averages for Scalable Robust PCA

A semi-supervised framework for mapping data to the intrinsic manifold

Inductive Hashing on Manifolds

Model Transport: Towards Scalable Transfer Learning on Manifolds

Towards Scalable Transfer Learning on Manifolds (2014)

On the Quotient Representation for the Essential Manifold

Model Transport: Towards Scalable Transfer Learning on Manifolds