5 citations · 10 across the 5 of their papers we have counts for
5 papers · 1 filter
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…
Deep Neural Networks with Efficient Guaranteed Invariances
Matthias Rath, Alexandru Paul Condurache
We address the problem of improving the performance and in particular the sample complexity of deep neural networks by enforcing and guaranteeing invariances to symmetry transforma…
Improving the Sample-Complexity of Deep Classification Networks with Invariant Integration
Matthias Rath, Alexandru Paul Condurache
Leveraging prior knowledge on intraclass variance due to transformations is a powerful method to improve the sample complexity of deep neural networks. This makes them applicable t…
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…