ECE
ECE
ECE ECE


CSP Seminar

Convolutional Dictionary Learning Using a Fast Block Proximal Gradient Method

Il Yong Chun


Post Doctoral Researcher
University of Michigan, Department of EECS
 
Thursday, April 13, 2017
4:00pm - 5:00pm
1005 EECS

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About the Event

Multi-convex optimization problems play a significant role in modern signal/image processing, computer vision, and related disciplines. The Block Proximal Gradient (BPG) method is a state- of-the-art technique for solving multi-convex problems that avoids small regions around certain local minima and provides lower objective values. However, the BPG technique can be impractical for solving complicated and/or large dimensional problems, due to its dependence on Lipschitz continuity of the objective function. The first half of this talk introduces our recent optimization method, BPG method using a Majorizer (BPG-M), that has the benefits of resolving drawbacks of BPG, and its accelerated versions. Convolutional Dictionary Learning (CDL) is a fundamental component in understanding (deep) convolutional neural networks. In addition, CDL has received considerable attention by resolving the fundamental problems of patch-based dictionary learning, e.g., translation-variant dictionaries and limitations using “big data”. The popular optimization techniques for solving bi-convex prob- lems of CDL are the augmented Lagrangian method and its variants. However, they require tricky parameter tuning processes due to their dependence on training data and lack of convergence guar- antee. The second half of this talk introduces a CDL formulation and demonstrates the usefulness of applying fast BPG-M for convergent CDL. This is joint work work with Xuehang Zheng and Jeffrey A. Fessler

Biography

Il Yong Chun received the Ph.D. degree in electrical and computer engineering from Purdue Uni- versity, West Lafayette, IN, USA, in 2015. From 2015 to 2016, he was a Postdoctoral Research Associate in Mathematics, Purdue University, West Lafayette, IN, USA. He is currently a Post- doctoral Research Fellow in Engineering and Computer Science, the University of Michigan, Ann Arbor, MI, USA. His research interests include compressed sensing, non-convex optimization, convolutional kernel learning, and adaptive signal processing, applied to computational imaging in medicine, photography, and neuroscience.

Additional Information

Contact: Judi Jones

Phone: 763-8557

Email: asap@umich.edu

Sponsor(s): ECE - Systems

Faculty Sponsor: Dave Neuhoff

Open to: Public