Albatross

Last updated on Oct 7, 2024
Go to Project Site
Deep Learning Continual Learning
Tobias Christian Nauen
Tobias Christian Nauen
PhD Student

My research interests include efficiency of machine learning models, multimodal learning, and transformer models.

Publications

When 512×512 is not Enough: Local Degradation-Aware Multi-Diffusion for Extreme Image Super-Resolution
We extend pretrained super-resolution models to larger images by using local-aware prompts.
Brian B. Moser, Stanislav Frolov, Tobias Christian Nauen, Federico Raue, Andreas Dengel
PDF Cite Code Project Project Project DOI
When 512×512 is not Enough: Local Degradation-Aware Multi-Diffusion for Extreme Image Super-Resolution
ForAug: Recombining Foregrounds and Backgrounds to Improve Vision Transformer Training with Bias Mitigation
We improve the training of vision transformers by segmenting and recombining objects and backgrounds from datasets. This makes the transformers more accurate, as well as more robust.
Tobias Christian Nauen, Brian Moser, Federico Raue, Stanislav Frolov, Andreas Dengel
PDF Cite Code Dataset Project Project Project Supplementary Material
ForAug: Recombining Foregrounds and Backgrounds to Improve Vision Transformer Training with Bias Mitigation
A Study in Dataset Distillation for Image Super-Resolution
We conduct the first systematic study of dataset distillation for Super-Resolution.
Tobias Dietz, Brian Bernhard Moser, Tobias Christian Nauen, Federico Raue, Stanislav Frolov, Andreas Dengel
PDF Cite Project Project
A Study in Dataset Distillation for Image Super-Resolution
Distill the Best, Ignore the Rest: Improving Dataset Distillation with Loss-Value-Based Pruning
We improve dataset distillation by distilling only a representative coreset.
Brian Bernhard Moser, Federico Raue, Tobias Christian Nauen, Stanislav Frolov, Andreas Dengel
PDF Cite Code Project Project Project
Distill the Best, Ignore the Rest: Improving Dataset Distillation with Loss-Value-Based Pruning
Just Leaf It: Accelerating Diffusion Classifiers with Hierarchical Class Pruning
We speed up diffusion classifiers by utilizing a label hierarchy and pruning unrelated paths.
Arundhati S Shanbhag, Brian Bernhard Moser, Tobias Christian Nauen, Stanislav Frolov, Federico Raue, Andreas Dengel
PDF Cite Project Project Project
Just Leaf It: Accelerating Diffusion Classifiers with Hierarchical Class Pruning
A Low-Resolution Image is Worth 1x1 Words: Enabling Fine Image Super-Resolution with Transformers and TaylorShift
We utilize the TaylorShift attention mechanism for global pixel-wise-attention in image super-resolution.
Sanath Budakegowdanadoddi Nagaraju, Brian Bernhard Moser, Tobias Christian Nauen, Stanislav Frolov, Federico Raue, Andreas Dengel
PDF Cite Project Project Project
A Low-Resolution Image is Worth 1x1 Words: Enabling Fine Image Super-Resolution with Transformers and TaylorShift