Measuring the Effect of Causal Disentanglement on the Adversarial Robustness of Neural Network Models-Reference-Cited by-同舟云学术

Measuring the Effect of Causal Disentanglement on the Adversarial Robustness of Neural Network Models

Published:2023-10-21 Issue: Volume: Page:
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Container-title:Proceedings of the 32nd ACM International Conference on Information and Knowledge Management
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Author:

Ness Preben M.¹^ORCID,Marijan Dusica¹^ORCID,Bose Sunanda¹^ORCID

Affiliation:

1. Simula Research Laboratory, Oslo, Norway

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3583780.3614960

Reference40 articles.

1. Yoshua Bengio , Aaron Courville , and Pascal Vincent . 2013. Representation learning: A review and new perspectives . IEEE transactions on pattern analysis and machine intelligence, Vol. 35 , 8 ( 2013 ), 1798--1828. Yoshua Bengio, Aaron Courville, and Pascal Vincent. 2013. Representation learning: A review and new perspectives. IEEE transactions on pattern analysis and machine intelligence, Vol. 35, 8 (2013), 1798--1828.

2. Marc-André Carbonneau , Julian Zaidi , Jonathan Boilard , and Ghyslain Gagnon . 2022. Measuring disentanglement: A review of metrics . IEEE Transactions on Neural Networks and Learning Systems ( 2022 ). Marc-André Carbonneau, Julian Zaidi, Jonathan Boilard, and Ghyslain Gagnon. 2022. Measuring disentanglement: A review of metrics. IEEE Transactions on Neural Networks and Learning Systems (2022).

3. Nicholas Carlini and David Wagner. 2017. Towards evaluating the robustness of neural networks. In 2017 ieee symposium on security and privacy (sp). Ieee 39--57. Nicholas Carlini and David Wagner. 2017. Towards evaluating the robustness of neural networks. In 2017 ieee symposium on security and privacy (sp). Ieee 39--57.

4. Kien Do and Truyen Tran . 2019 . Theory and Evaluation Metrics for Learning Disentangled Representations. In International Conference on Learning Representations. Kien Do and Truyen Tran. 2019. Theory and Evaluation Metrics for Learning Disentangled Representations. In International Conference on Learning Representations.