401-4661-72L  Robustness of Deep Neural Networks

SemesterAutumn Semester 2022
LecturersR. Alaifari
Periodicitynon-recurring course
Language of instructionEnglish


401-4661-72 GRobustness of Deep Neural Networks2 hrs
Thu14:15-16:00RZ F 21 »
R. Alaifari
401-4661-72 ARobustness of Deep Neural Networks1 hrsR. Alaifari

Catalogue data

AbstractWhile deep neural networks have been very successfully employed in classification problems, their stability properties remain still unclear. In particular, the presence of adversarial examples has demonstrated that state-of-the-art networks are vulnerable to small perturbations in the data. This course serves as an introduction to adversarial attacks and defenses for deep neural nework algorithms.
Objective1. Theory: in this course, we will discuss the trade-off between accuracy and stability of classification algorithms and study the state-of-the-art for robust image classification, adversarial attacks and adversarial training.
2. Practice: students will train and attack deep neural networks themselves, to get a hands-on experience.
Prerequisites / NoticeCourses on linear algebra, optimization and machine learning. Basic programming skills in Python and experience with PyTorch.

Performance assessment

Performance assessment information (valid until the course unit is held again)
Performance assessment as a semester course
ECTS credits6 credits
ExaminersR. Alaifari
Typegraded semester performance
Language of examinationEnglish
RepetitionRepetition possible without re-enrolling for the course unit.

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Offered in

Mathematics MasterSelection: Numerical AnalysisWInformation