3 citations · 7 across the 7 of their papers we have counts for
3 papers · 1 filter
Large-Scale Dataset Pruning in Adversarial Training through Data Importance Extrapolation
Björn Nieth, Thomas Altstidl, Leo Schwinn +1
Their vulnerability to small, imperceptible attacks limits the adoption of deep learning models to real-world systems. Adversarial training has proven to be one of the most promisi…
How Intermodal Interaction Affects the Performance of Deep Multimodal Fusion for Mixed-Type Time Series
Simon Dietz, Thomas Altstidl, Dario Zanca +2
Mixed-type time series (MTTS) is a bimodal data type that is common in many domains, such as healthcare, finance, environmental monitoring, and social media. It consists of regular…
Raising the Bar for Certified Adversarial Robustness with Diffusion Models
Thomas Altstidl, David Dobre, Björn Eskofier +2
Certified defenses against adversarial attacks offer formal guarantees on the robustness of a model, making them more reliable than empirical methods such as adversarial training,…