3 papers
cs.CV2025
Stylized Synthetic Augmentation further improves Corruption Robustness
Georg Siedel, Rojan Regmi, Abhirami Anand +3
This paper proposes a training data augmentation pipeline that combines synthetic image data with neural style transfer in order to address the vulnerability of deep vision models…
cs.CV2025
Combined Image Data Augmentations diminish the benefits of Adaptive Label Smoothing
Georg Siedel, Ekagra Gupta, Weijia Shao +2
Soft augmentation regularizes the supervised learning process of image classifiers by reducing label confidence of a training sample based on the magnitude of random-crop augmentat…
cs.LG2024
A practical approach to evaluating the adversarial distance for machine learning classifiers
Georg Siedel, Ekagra Gupta, Andrey Morozov
Robustness is critical for machine learning (ML) classifiers to ensure consistent performance in real-world applications where models may encounter corrupted or adversarial inputs.…