필터 지우기
필터 지우기

How are the features obtained in a sparse autoencoder?

조회 수: 2 (최근 30일)
Rohan Pathak
Rohan Pathak 2017년 12월 5일
답변: BERGHOUT Tarek 2019년 4월 9일
https://www.mathworks.com/help/nnet/examples/training-a-deep-neural-network-for-digit-classification.html
In the above tutorial, how do we get the image features in the first hidden layer?
This is a homework question and I can't seem to figure out how exactly the trainAutoencoder function is carrying out the feature extraction. Like, it has to go through some feature detection, followed by a feature extraction algorithm, right? Is that what it's doing?
NOTE: The original question is: How were the features in Fig 3 obtained? Fig 3 refers to the features learned by the autoencoder representing curls and stroke patterns from the digit images.

답변 (1개)

BERGHOUT Tarek
BERGHOUT Tarek 2019년 4월 9일
in spearse autoencoders , a set of the original images mapped to the output layer passing by the hidden layer, where the outputs inintialy is the same as the input (g(H)=x) and H is the hidden layer.
but in sparse auto encoder the hidden layer is not the as hidden layer in ordinary autoencoder; the hidden layer must be 'sparse': contains the maximam number of Zeros, that is mean we will code the input with only the significant features in the hidden layer.
go check it.

카테고리

Help CenterFile Exchange에서 Image Data Workflows에 대해 자세히 알아보기

Community Treasure Hunt

Find the treasures in MATLAB Central and discover how the community can help you!

Start Hunting!

Translated by