5 citations · 6 across the 3 of their papers we have counts for
3 papers
cs.LG2023
GIT: Detecting Uncertainty, Out-Of-Distribution and Adversarial Samples using Gradients and Invariance Transformations
Julia Lust, Alexandru P. Condurache
Deep neural networks tend to make overconfident predictions and often require additional detectors for misclassifications, particularly for safety-critical applications. Existing d…
cs.LG2020★ 1 cited
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…
cs.LG2020★ 5 cited
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…