Adversarially Robust Learning via Entropic Regularization

In this paper we propose a new family of algorithms, ATENT, for training adversarially robust deep neural networks. We formulate a new loss function that is equipped with an additional entropic regularization. Our loss function considers the contribution of adversarial samples that are drawn from a...

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Detalles Bibliográficos
Autores principales: Jagatap, Gauri, Joshi, Ameya, Chowdhury, Animesh Basak, Garg, Siddharth, Hegde, Chinmay
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Artificial Intelligence
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8764444/
https://www.ncbi.nlm.nih.gov/pubmed/35059637
http://dx.doi.org/10.3389/frai.2021.780843

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8764444/
https://www.ncbi.nlm.nih.gov/pubmed/35059637
http://dx.doi.org/10.3389/frai.2021.780843

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