Error-correcting output codes with ensemble diversity for robust learning in neural networks

Error-correcting output codes with ensemble diversity for robust learning in neural networks

Though deep learning has been applied successfully in many scenarios, malicious inputs with human-imperceptible perturbations can make it vulnerable in real applications. This paper proposes an error-correcting neural network (ECNN) that combines a set of binary classifiers to combat adversarial exam...

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Bibliographic Details
Main Authors: Song, Yang, Kang, Qiyu, Tay, Wee Peng
Other Authors: School of Electrical and Electronic Engineering
Format: Conference Paper
Language:English
Published: 2021
Subjects:
Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence
Deep Learning
Neural Networks
Online Access:https://hdl.handle.net/10356/147336

Internet

https://hdl.handle.net/10356/147336

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