EE Seminar: Learned Convolutional Sparse Coding

Speaker:  Hillel Sreter

M.Sc. student under the supervision of Dr. Raja Giryes

Sunday, December 30^th, 2018 at 15:00

Room 011, Kitot Bldg., Faculty of Engineering

Learned Convolutional Sparse Coding
 

Abstract

    We propose a convolutional recurrent sparse auto-encoder model. The model consists of a sparse encoder, which is a convolutional extension of the learned ISTA (LISTA) method, and a linear convolutional decoder.

    Our strategy offers a simple method for learning a task-driven sparse convolutional dictionary (CD) and producing an approximate convolutional sparse code (CSC) over the learned dictionary.

    We trained the model to minimize reconstruction loss via gradient decent

with back-propagation and have achieved competitive results to KSVD image denoising and to leading CSC methods in image inpainting requiring only a small fraction of their run-time.