Denoised Internal Models: a Brain-Inspired Autoencoder against Adversarial Attacks

Despite its great success, deep learning severely suffers from robustness; that is, deep neural networks are very vulnerable to adversarial attacks, even the simplest ones. Inspired by recent advances in brain science, we propose the Denoised Internal Models (DIM), a novel generative autoencoder-bas...

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Bibliographic Details

Published in:	arXiv.org
Main Authors:	Liu, Kaiyuan, Li, Xingyu, Lai, Yurui, Zhang, Ge, Su, Hang, Wang, Jiachen, Guo, Chunxu, Guan, Jisong, Zhou, Yi
Format:	Paper
Language:	English
Published:	Ithaca Cornell University Library, arXiv.org 05.03.2023
Subjects:	Artificial neural networks Brain Machine learning Noise reduction Robustness Signal processing Thalamus Visual signals
ISSN:	2331-8422
Online Access:	Get full text
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