8 citations · 13 across the 8 of their papers we have counts for
6 papers · 1 filter
HydraMix: Multi-Image Feature Mixing for Small Data Image Classification
Christoph Reinders, Frederik Schubert, Bodo Rosenhahn
Training deep neural networks requires datasets with a large number of annotated examples. The collection and annotation of these datasets is not only extremely expensive but also…
RAW-Diffusion: RGB-Guided Diffusion Models for High-Fidelity RAW Image Generation
Christoph Reinders, Radu Berdan, Beril Besbinar +2
Current deep learning approaches in computer vision primarily focus on RGB data sacrificing information. In contrast, RAW images offer richer representation, which is crucial for p…
Compensation Learning in Semantic Segmentation
Timo Kaiser, Christoph Reinders, Bodo Rosenhahn
Label noise and ambiguities between similar classes are challenging problems in developing new models and annotating new data for semantic segmentation. In this paper, we propose C…
Blind Knowledge Distillation for Robust Image Classification
Timo Kaiser, Lukas Ehmann, Christoph Reinders +1
Optimizing neural networks with noisy labels is a challenging task, especially if the label set contains real-world noise. Networks tend to generalize to reasonable patterns in the…
ChimeraMix: Image Classification on Small Datasets via Masked Feature Mixing
Christoph Reinders, Frederik Schubert, Bodo Rosenhahn
Deep convolutional neural networks require large amounts of labeled data samples. For many real-world applications, this is a major limitation which is commonly treated by augmenta…
Object Recognition from very few Training Examples for Enhancing Bicycle Maps
Christoph Reinders, Hanno Ackermann, Michael Ying Yang +1
In recent years, data-driven methods have shown great success for extracting information about the infrastructure in urban areas. These algorithms are usually trained on large data…