3 papers
cs.LG2025
TRIX- Trading Adversarial Fairness via Mixed Adversarial Training
Tejaswini Medi, Steffen Jung, Margret Keuper
Adversarial Training (AT) is a widely adopted defense against adversarial examples. However, existing approaches typically apply a uniform training objective across all classes, ov…
cs.LG2024
Towards Class-wise Robustness Analysis
Tejaswini Medi, Julia Grabinski, Margret Keuper
While being very successful in solving many downstream tasks, the application of deep neural networks is limited in real-life scenarios because of their susceptibility to domain sh…
cs.CV2024
3D-WAG: Hierarchical Wavelet-Guided Autoregressive Generation for High-Fidelity 3D Shapes
Tejaswini Medi, Arianna Rampini, Pradyumna Reddy +2
Autoregressive (AR) models have achieved remarkable success in natural language and image generation, but their application to 3D shape modeling remains largely unexplored. Unlike…