Flipover,an enhanced dropout technique,is introduced to improve the robustness of artificial neural networks.In contrast to dropout,which involves randomly removing certain neurons and their connections,flipover rando...Flipover,an enhanced dropout technique,is introduced to improve the robustness of artificial neural networks.In contrast to dropout,which involves randomly removing certain neurons and their connections,flipover randomly selects neurons and reverts their outputs using a negative multiplier during training.This approach offers stronger regularization than conventional dropout,refining model performance by(1)mitigating overfitting,matching or even exceeding the efficacy of dropout;(2)amplifying robustness to noise;and(3)enhancing resilience against adversarial attacks.Extensive experiments across various neural networks affirm the effectiveness of flipover in deep learning.展开更多
基金supported in part by the National Institutes of Health,Nos.R01CA237267,R01HL151561,R01EB031102,and R01EB032716.
文摘Flipover,an enhanced dropout technique,is introduced to improve the robustness of artificial neural networks.In contrast to dropout,which involves randomly removing certain neurons and their connections,flipover randomly selects neurons and reverts their outputs using a negative multiplier during training.This approach offers stronger regularization than conventional dropout,refining model performance by(1)mitigating overfitting,matching or even exceeding the efficacy of dropout;(2)amplifying robustness to noise;and(3)enhancing resilience against adversarial attacks.Extensive experiments across various neural networks affirm the effectiveness of flipover in deep learning.
