25 citations · 60 across the 12 of their papers we have counts for
5 papers · 1 filter
FreezeNet: Full Performance by Reduced Storage Costs
Paul Wimmer, Jens Mehnert, Alexandru Condurache
Pruning generates sparse networks by setting parameters to zero. In this work we improve one-shot pruning methods, applied before training, without adding any additional storage co…
A Survey on Assessing the Generalization Envelope of Deep Neural Networks: Predictive Uncertainty, Out-of-distribution and Adversarial Samples
Julia Lust, Alexandru Paul Condurache
Deep Neural Networks (DNNs) achieve state-of-the-art performance on numerous applications. However, it is difficult to tell beforehand if a DNN receiving an input will deliver the…
Boosting Deep Neural Networks with Geometrical Prior Knowledge: A Survey
Matthias Rath, Alexandru Paul Condurache
Deep Neural Networks achieve state-of-the-art results in many different problem settings by exploiting vast amounts of training data. However, collecting, storing and - in the case…
GraN: An Efficient Gradient-Norm Based Detector for Adversarial and Misclassified Examples
Julia Lust, Alexandru Paul Condurache
Deep neural networks (DNNs) are vulnerable to adversarial examples and other data perturbations. Especially in safety critical applications of DNNs, it is therefore crucial to dete…
Invariant Integration in Deep Convolutional Feature Space
Matthias Rath, Alexandru Paul Condurache
In this contribution, we show how to incorporate prior knowledge to a deep neural network architecture in a principled manner. We enforce feature space invariances using a novel la…