107 citations · 131 across the 44 of their papers we have counts for
14 papers · 1 filter
PRISM: Diversifying Dataset Distillation by Decoupling Architectural Priors
Brian B. Moser, Shalini Sarode, Federico Raue +6
Dataset distillation (DD) promises compact yet faithful synthetic data, but existing approaches often inherit the inductive bias of a single teacher model. As dataset size increase…
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…
Towards Facilitated Fairness Assessment of AI-based Skin Lesion Classifiers Through GenAI-based Image Synthesis
Ko Watanabe, Stanislav Frolov, Aya Hassan +3
Recent advances in deep learning and on-device inference could transform routine screening for skin cancers. Along with the anticipated benefits of this technology, potential dange…
Unifying VXAI: A Systematic Review and Framework for the Evaluation of Explainable AI
David Dembinsky, Adriano Lucieri, Stanislav Frolov +3
Modern AI systems frequently rely on opaque black-box models, most notably Deep Neural Networks, whose performance stems from complex architectures with millions of learned paramet…
How do Scaling Laws Apply to Knowledge Graph Engineering Tasks? The Impact of Model Size on Large Language Model Performance
Desiree Heim, Lars-Peter Meyer, Markus Schröder +2
When using Large Language Models (LLMs) to support Knowledge Graph Engineering (KGE), one of the first indications when searching for an appropriate model is its size. According to…
A Coreset Selection of Coreset Selection Literature: Introduction and Recent Advances
Brian B. Moser, Arundhati S. Shanbhag, Stanislav Frolov +3
Coreset selection targets the challenge of finding a small, representative subset of a large dataset that preserves essential patterns for effective machine learning. Although seve…
Evaluating Generative Models for Tabular Data: Novel Metrics and Benchmarking
Dayananda Herurkar, Ahmad Ali, Andreas Dengel
Generative models have revolutionized multiple domains, yet their application to tabular data remains underexplored. Evaluating generative models for tabular data presents unique c…
Tabular Data Adapters: Improving Outlier Detection for Unlabeled Private Data
Dayananda Herurkar, Jörn Hees, Vesselin Tzvetkov +1
The remarkable success of Deep Learning approaches is often based and demonstrated on large public datasets. However, when applying such approaches to internal, private datasets, o…
EMF: Event Meta Formers for Event-based Real-time Traffic Object Detection
Muhammad Ahmed Ullah Khan, Abdul Hannan Khan, Andreas Dengel
Event cameras have higher temporal resolution, and require less storage and bandwidth compared to traditional RGB cameras. However, due to relatively lagging performance of event-b…
Spherical Dense Text-to-Image Synthesis
Timon Winter, Stanislav Frolov, Brian Bernhard Moser +1
Recent advancements in text-to-image (T2I) have improved synthesis results, but challenges remain in layout control and generating omnidirectional panoramic images. Dense T2I (DT2I…
ForAug: Mitigating Biases in Image Classification via Controlled Image Compositions
Tobias Christian Nauen, Brian Moser, Federico Raue +2
Large-scale image classification datasets exhibit strong compositional biases: objects tend to be centered, appear at characteristic scales, and co-occur with class-specific contex…
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…
Multi-Label Scene Classification in Remote Sensing Benefits from Image Super-Resolution
Ashitha Mudraje, Brian B. Moser, Stanislav Frolov +1
Satellite imagery is a cornerstone for numerous Remote Sensing (RS) applications; however, limited spatial resolution frequently hinders the precision of such systems, especially i…