Methoden vergelijken

	Adversariële training	Transferleren
Vakgebied	Deep learning	Machine learning
Familie	Machine learning	Machine learning
Jaar van ontstaan	2018	2010 (formalized); 1990s (early roots)
Grondlegger	Aleksander Madry et al.	Pan, S. J. & Yang, Q. (survey); Bengio, Y. (deep learning framing)
Type	Robust optimization training procedure	Learning paradigm
Oorspronkelijke bron	Madry, A., Makelov, A., Schmidt, L., Tsipras, D., & Vladu, A. (2018). Towards deep learning models resistant to adversarial attacks. International Conference on Learning Representations (ICLR).	Pan, S. J., & Yang, Q. (2010). A Survey on Transfer Learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359.
Aliassen	Min-Max Robust Training, PGD Adversarial Training, Robust Empirical Risk Minimization, Hasımsal Eğitim	TL, domain adaptation, fine-tuning, pre-trained model adaptation
Verwant	3	3
Samenvatting	Adversarial Training is a robust optimization procedure for deep neural networks in which the model is trained not on clean data alone but on worst-case perturbed inputs crafted during training. Formalized by Madry et al. (2018) as a min-max saddle-point problem, the method uses Projected Gradient Descent (PGD) to generate strong adversarial examples within a bounded Lp perturbation set before each gradient update, forcing the network to learn decision boundaries that are stable under such perturbations.	Transfer learning is a machine learning paradigm in which knowledge gained from training a model on a source task or domain is reused to improve learning on a different but related target task or domain. It is especially powerful when labeled data for the target task is scarce, and it underlies most modern deep learning applications in computer vision, natural language processing, and beyond.