7 papers
SubZeroCore: A Submodular Approach with Zero Training for Coreset Selection
Brian B. Moser, Tobias C. Nauen, Arundhati S. Shanbhag +4
The goal of coreset selection is to identify representative subsets of datasets for efficient model training. Yet, existing approaches paradoxically require expensive training-base…
HyperCore: Coreset Selection under Noise via Hypersphere Models
Brian B. Moser, Arundhati S. Shanbhag, Tobias C. Nauen +4
The goal of coreset selection methods is to identify representative subsets of datasets for efficient model training. Yet, existing methods often ignore the possibility of annotati…
A Study in Dataset Distillation for Image Super-Resolution
Tobias Dietz, Brian B. Moser, Tobias Nauen +3
Dataset distillation aims to compress large datasets into compact yet highly informative subsets that preserve the training behavior of the original data. While this concept has ga…
Distill the Best, Ignore the Rest: Improving Dataset Distillation with Loss-Value-Based Pruning
Brian B. Moser, Federico Raue, Tobias C. Nauen +2
Dataset distillation has gained significant interest in recent years, yet existing approaches typically distill from the entire dataset, potentially including non-beneficial sample…
Zoomed In, Diffused Out: Towards Local Degradation-Aware Multi-Diffusion for Extreme Image Super-Resolution
Brian B. Moser, Stanislav Frolov, Tobias C. Nauen +2
Large-scale, pre-trained Text-to-Image (T2I) diffusion models have gained significant popularity in image generation tasks and have shown unexpected potential in image Super-Resolu…
Just Leaf It: Accelerating Diffusion Classifiers with Hierarchical Class Pruning
Arundhati S. Shanbhag, Brian B. Moser, Tobias C. Nauen +3
Diffusion models, celebrated for their generative capabilities, have recently demonstrated surprising effectiveness in image classification tasks by using Bayes' theorem. Yet, curr…