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Convolutional Factor Analysis Inspired Compressvie Sensing

Citation Author(s):
Yunchen Pu
Submitted by:
Xin YUAN
Last updated:
17 September 2017 - 11:10am
Document Type:
Presentation Slides
Document Year:
2017
Event:
Presenters Name:
Xin Yuan
Paper Code:
MQ-L2.1

Abstract 

Abstract: 

We solve the compressive sensing problem via convolutional factor analysis, where the convolutional dictionaries are learned in situ from the compressed measurements. An alternating direction method of multipliers (ADMM) paradigm for compressive sensing inversion based on convolutional factor analysis is developed. The proposed algorithm provides reconstructed images as well as features, which can be directly used for recognition (e:g:, classification) tasks. We demonstrate that using  30% (relative to pixel numbers) compressed measurements, the proposed model achieves the classification accuracy comparable to the original data on MNIST.

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