FrequencyLowCut Pooling - Plug and Play Against Catastrophic Overfitting. - Julia Grabinski; Steffen Jung 001; Janis Keuper; Margret Keuper (ECCV (14), 2022)

Robust Models are less Over-Confident. - Julia Grabinski; Paul Gavrikov; Janis Keuper; Margret Keuper (NeurIPS, 2022)

FrequencyLowCut Pooling - Plug & Play against Catastrophic Overfitting. - Julia Grabinski; Steffen Jung 001; Janis Keuper; Margret Keuper (CoRR, 2022)

Aliasing and adversarial robust generalization of CNNs. - Julia Grabinski; Janis Keuper; Margret Keuper (Mach. Learn., 2022)

On the unreasonable vulnerability of transformers for image restoration - and an easy fix. - Shashank Agnihotri; Kanchana Vaishnavi Gandikota; Julia Grabinski; Paramanand Chandramouli; Margret Keuper (ICCV (Workshops), 2023)

Fix your downsampling ASAP! Be natively more robust via Aliasing and Spectral Artifact free Pooling. - Julia Grabinski; Janis Keuper; Margret Keuper (CoRR, 2023)

As large as it gets: Learning infinitely large Filters via Neural Implicit Functions in the Fourier Domain. - Julia Grabinski; Janis Keuper; Margret Keuper (CoRR, 2023)

Improving Stability during Upsampling - on the Importance of Spatial Context. - Shashank Agnihotri; Julia Grabinski; Margret Keuper (CoRR, 2023)

Improving Feature Stability During Upsampling - Spectral Artifacts and the Importance of Spatial Context. - Shashank Agnihotri; Julia Grabinski; Margret Keuper (ECCV (58), 2024)

Beware of Aliases - Signal Preservation is Crucial for Robust Image Restoration. - Shashank Agnihotri; Julia Grabinski; Janis Keuper; Margret Keuper (CoRR, 2024)

Towards Class-wise Robustness Analysis. - Tejaswini Medi; Julia Grabinski; Margret Keuper (CoRR, 2024)

As large as it gets - Studying Infinitely Large Convolutions via Neural Implicit Frequency Filters. - Julia Grabinski; Janis Keuper; Margret Keuper (Trans. Mach. Learn. Res., 2024)