- Standort
-
Deutsche Nationalbibliothek Frankfurt am Main
- Umfang
-
Online-Ressource
- Sprache
-
Englisch
- Anmerkungen
-
Tübingen, Universität Tübingen, Dissertation, 2025
- Schlagwort
-
Robustheit
Maschinelles Lernen
- Ereignis
-
Veröffentlichung
- (wo)
-
Tübingen
- (wer)
-
Universitätsbibliothek Tübingen
- (wann)
-
2025
- Urheber
- Beteiligte Personen und Organisationen
-
Hein, Matthias
- URN
-
urn:nbn:de:bsz:21-dspace-1616366
- Rechteinformation
-
Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Letzte Aktualisierung
-
15.08.2025, 07:32 MESZ
Datenpartner
Dieses Objekt wird bereitgestellt von:
Deutsche Nationalbibliothek. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Deutsche Nationalbibliothek. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Beteiligte
- Voráček, Václav
- Hein, Matthias
- Universitätsbibliothek Tübingen
Entstanden
- 2025
Ähnliche Objekte (12)
Adversarial attacks and adversarial robustness in computational pathology
Sliced Wasserstein adversarial training for improving adversarial robustness
Adversarial attacks and adversarial robustness in computational pathology
Adversarial Robustness of Graph Neural Networks
Adversarial robustness improvement for deep neural networks
Adversarial classification via distributional robustness with Wasserstein ambiguity
Robustness in Machine Learning: Adversarial Perturbations, Explanations & Intuition
Advances in Reliably Evaluating and Improving Adversarial Robustness
Robustness and Domain Generalization
Boosting adversarial robustness via feature refinement, suppression, and alignment
Robustness to adversarial examples can be improved with overfitting
Improving Robustness of Jet Tagging Algorithms with Adversarial Training
Adversarial attacks and adversarial robustness in computational pathology
Sliced Wasserstein adversarial training for improving adversarial robustness
Adversarial attacks and adversarial robustness in computational pathology
Adversarial Robustness of Graph Neural Networks
Adversarial robustness improvement for deep neural networks
Adversarial classification via distributional robustness with Wasserstein ambiguity
Robustness in Machine Learning: Adversarial Perturbations, Explanations & Intuition
Advances in Reliably Evaluating and Improving Adversarial Robustness
Robustness and Domain Generalization
Boosting adversarial robustness via feature refinement, suppression, and alignment
Robustness to adversarial examples can be improved with overfitting
Improving Robustness of Jet Tagging Algorithms with Adversarial Training
Adversarial attacks and adversarial robustness in computational pathology
Sliced Wasserstein adversarial training for improving adversarial robustness
Adversarial attacks and adversarial robustness in computational pathology
Adversarial Robustness of Graph Neural Networks
Adversarial robustness improvement for deep neural networks
Adversarial classification via distributional robustness with Wasserstein ambiguity
Robustness in Machine Learning: Adversarial Perturbations, Explanations & Intuition
Advances in Reliably Evaluating and Improving Adversarial Robustness
Robustness and Domain Generalization
Boosting adversarial robustness via feature refinement, suppression, and alignment
Robustness to adversarial examples can be improved with overfitting