65 citations · 132 across the 23 of their papers we have counts for
4 papers · 1 filter
Core Tokensets for Data-efficient Sequential Training of Transformers
Subarnaduti Paul, Manuel Brack, Patrick Schramowski +2
Deep networks are frequently tuned to novel tasks and continue learning from ongoing data streams. Such sequential training requires consolidation of new and past information, a ch…
Designing a Hybrid Neural System to Learn Real-world Crack Segmentation from Fractal-based Simulation
Achref Jaziri, Martin Mundt, Andres Fernandez Rodriguez +1
Identification of cracks is essential to assess the structural integrity of concrete infrastructure. However, robust crack segmentation remains a challenging task for computer visi…
Meta-learning Convolutional Neural Architectures for Multi-target Concrete Defect Classification with the COncrete DEfect BRidge IMage Dataset
Martin Mundt, Sagnik Majumder, Sreenivas Murali +2
Recognition of defects in concrete infrastructure, especially in bridges, is a costly and time consuming crucial first step in the assessment of the structural integrity. Large var…
Building effective deep neural network architectures one feature at a time
Martin Mundt, Tobias Weis, Kishore Konda +1
Successful training of convolutional neural networks is often associated with sufficiently deep architectures composed of high amounts of features. These networks typically rely on…