3 citations · 5 across the 4 of their papers we have counts for
4 papers
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…
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…
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…