27 citations · 30 across the 9 of their papers we have counts for
10 papers
Useful nonrobust features are ubiquitous in biomedical images
Coenraad Mouton, Randle Rabe, Niklas C. Koser +4
We study whether deep networks for medical imaging learn useful nonrobust features - predictive input patterns that are not human interpretable and highly susceptible to small adve…
Does simple trump complex? Comparing strategies for adversarial robustness in DNNs
William Brooks, Marelie H. Davel, Coenraad Mouton
Deep Neural Networks (DNNs) have shown substantial success in various applications but remain vulnerable to adversarial attacks. This study aims to identify and isolate the compone…
Impact of Batch Normalization on Convolutional Network Representations
Hermanus L. Potgieter, Coenraad Mouton, Marelie H. Davel
Batch normalization (BatchNorm) is a popular layer normalization technique used when training deep neural networks. It has been shown to enhance the training speed and accuracy of…
Is network fragmentation a useful complexity measure?
Coenraad Mouton, Randle Rabe, Daniël G. Haasbroek +3
It has been observed that the input space of deep neural network classifiers can exhibit `fragmentation', where the model function rapidly changes class as the input space is trave…
On margin-based generalization prediction in deep neural networks
Coenraad Mouton
Understanding generalization in deep neural networks is an active area of research. A promising avenue of exploration has been that of margin measurements: the shortest distance to…
Input margins can predict generalization too
Coenraad Mouton, Marthinus W. Theunissen, Marelie H. Davel
Understanding generalization in deep neural networks is an active area of research. A promising avenue of exploration has been that of margin measurements: the shortest distance to…
The Missing Margin: How Sample Corruption Affects Distance to the Boundary in ANNs
Marthinus W. Theunissen, Coenraad Mouton, Marelie H. Davel
Classification margins are commonly used to estimate the generalization ability of machine learning models. We present an empirical study of these margins in artificial neural netw…
Exploring layerwise decision making in DNNs
Coenraad Mouton, Marelie H. Davel
While deep neural networks (DNNs) have become a standard architecture for many machine learning tasks, their internal decision-making process and general interpretability is still…
Tracking translation invariance in CNNs
Johannes C. Myburgh, Coenraad Mouton, Marelie H. Davel
Although Convolutional Neural Networks (CNNs) are widely used, their translation invariance (ability to deal with translated inputs) is still subject to some controversy. We explor…
Stride and Translation Invariance in CNNs
Coenraad Mouton, Johannes C. Myburgh, Marelie H. Davel
Convolutional Neural Networks have become the standard for image classification tasks, however, these architectures are not invariant to translations of the input image. This lack…