In this paper, we propose a simple and effective feature learning architecture for image classification that is based on very basic data processing ...
PCA LDANet: A Simple Feature Learning Method for Image Classification. Yu un Ge,Jiani Hu,Weihong Deng. Beijing University of Posts and Telecommunications. No ...
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What is PCANet?
Principal component analysis (PCA) [8] is a classic approach to abstract feature parameters and illustrate the probabilistic views of representation learning. .
In the proposed architecture, PCA is employed to learn multistage filter banks. It is followed by simple binary hashing and block histograms for indexing and ...
In this paper, we propose a very simple deep learning network for image classification that is based on very basic data processing components.
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Apr 14, 2014 · In this work, we propose a very simple deep learning network for image classification which comprises only the very basic data processing components.
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PCANet: A Simple Deep Learning Baseline for Image Classification?
dl.acm.org › doi › TIP.2015.2475625
In this paper, we propose a very simple deep learning network for image classification that is based on very basic data processing components: 1) cascaded ...
A novel deep neural network based on learning subspaces and convolutional neural network with applications in image classification.
Missing: LDANet: | Show results with:LDANet:
In this work, we propose a very simple deep learning network for image classification which comprises only the very basic data processing components: cascaded ...
Apr 21, 2019 · Very simple deep learning network with is made up of PCA, super interesting with binary hashing and block histogram for pooling.