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https://hdl.handle.net/2142/100059
Description
Title
Analysis of the two-part predictive coder
Author(s)
Yu, Lian
Contributor(s)
Moulin, Pierre
Issue Date
2018-05
Keyword(s)
Image Compression
Image Classification
Predictive Coding
Neural Networks
Abstract
The two-part predictive coding framework aims to compress signals while preserving feature quality for
analysis purposes. The change in feature vectors after the compression is treated as a prediction error and
is quantized using a classification centric quantizer. The classification centric quantizer is a vector quantizer
that minimizes classification error in the task of image classification. In this thesis, the method is applied
to the STL-10 dataset and a subset of the ILSVRC2012 dataset. The classification systems include a deep
hybrid neural network that consists of the scattering transform and the residual network, and an end-to-end
learned deep residual network.
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