A compound convolutional neural network used to process small perturbation

Input samples formed by deliberately adding subtle interference to the data set may cause the model to give false outputs with high confidence. To solve this problem, this paper proposes a deep learning method. Different interference sources can be considered to be independent of each other. Therefo...

Full description

Saved in:
Bibliographic Details
Published in:Journal of physics. Conference series 2022-03, Vol.2216 (1), p.12048
Main Authors: Ni, W.H, Xu, Shu
Format: Article
Language:English
Subjects:
Artificial neural networks
Datasets
Interference
Machine learning
Neural networks
Perturbation
Physics
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Input samples formed by deliberately adding subtle interference to the data set may cause the model to give false outputs with high confidence. To solve this problem, this paper proposes a deep learning method. Different interference sources can be considered to be independent of each other. Therefore, different networks are trained based on densenet for different interference sources. Then, the data set added to the combined interference item is sent to each neural network, and the output of each network is used as the input of the neural network in the next stage. The next stage of the neural network is used for multi-objective classification based on the output of the neural network. The output of the neural network is disturbed by adding different features to the input data. Because the depth network of each type of interference is independent, when introducing a new interference term, only the depth network and full connection layer of the new interference term need to be trained, rather than the whole network, which greatly simplifies the training process when introducing a new interference source.
ISSN:1742-6588
1742-6596
DOI:10.1088/1742-6596/2216/1/012048