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cs.CV2023★ 1 cited
HyperSparse Neural Networks: Shifting Exploration to Exploitation through Adaptive Regularization
Patrick Glandorf, Timo Kaiser, Bodo Rosenhahn
Sparse neural networks are a key factor in developing resource-efficient machine learning applications. We propose the novel and powerful sparse learning method Adaptive Regularize…
cs.CV2023
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…