most citedHyperSparse Neural Networks: Shifting Exploration to Exploitation through Adaptive Regularization

1 citations · 1 across the 2 of their papers we have counts for

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2 papers

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…