1 code implementation • 26 Apr 2023 • Timo Kaiser, Christoph Reinders, Bodo Rosenhahn
In this paper, we propose Compensation Learning in Semantic Segmentation, a framework to identify and compensate ambiguities as well as label noise.
1 code implementation • 21 Nov 2022 • Timo Kaiser, Lukas Ehmann, Christoph Reinders, Bodo Rosenhahn
We introduce Blind Knowledge Distillation - a novel teacher-student approach for learning with noisy labels by masking the ground truth related teacher output to filter out potentially corrupted knowledge and to estimate the tipping point from generalizing to overfitting.
1 code implementation • 23 Feb 2022 • Christoph Reinders, Frederik Schubert, Bodo Rosenhahn
In this work, we address the problem of learning deep neural networks on small datasets.
no code implementations • 25 Nov 2019 • Christoph Reinders, Bodo Rosenhahn
We present Neural Random Forest Imitation - a novel approach for transforming random forests into neural networks.
no code implementations • 18 Sep 2017 • Christoph Reinders, Hanno Ackermann, Michael Ying Yang, Bodo Rosenhahn
These algorithms are usually trained on large datasets consisting of thousands or millions of labeled training examples.