ON EXTENSIONS OF CLEVER: A NEURAL NETWORK ROBUSTNESS EVALUATION ALGORITHM
© 2018 IEEE. CLEVER (Cross-Lipschitz Extreme Value for nEtwork Robustness) is an Extreme Value Theory (EVT) based robustness score for large-scale deep neural networks (DNNs). In this paper, we propose two extensions on this robustness score. First, we provide a new formal robustness guarantee for c...
Main Authors: | Weng, Tsui-Wei (Author), Zhang, Huan (Author), Chen, Pin-Yu (Author), Lozano, Aurelie (Author), Hsieh, Cho-Jui (Author), Daniel, Luca (Author) |
---|---|
Format: | Article |
Language: | English |
Published: |
Institute of Electrical and Electronics Engineers (IEEE),
2022-02-03T19:42:27Z.
|
Subjects: | |
Online Access: | Get fulltext |
Similar Items
-
ON EXTENSIONS OF CLEVER: A NEURAL NETWORK ROBUSTNESS EVALUATION ALGORITHM
by: Weng, Tsui-Wei, et al.
Published: (2021) -
Efficient Neural Network Robustness Certification with General Activation Functions
by: Zhang, Huan, et al.
Published: (2021) -
POPQORN: Quantifying robustness of recurrent neural networks
by: Weng, Tsui-Wei, et al.
Published: (2021) -
Clever robot
by: Sobreira, Héber Miguel Plácido
Published: (2009) -
CNN-Cert: An Efficient Framework for Certifying Robustness of Convolutional Neural Networks
by: Boopathy, Akhilan, et al.
Published: (2021)