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Pruning by Explaining: A Novel Criterion for Deep Neural Network Pruning
Seul-Ki Yeom, Philipp Seegerer, Sebastian Lapuschkin +4
The success of convolutional neural networks (CNNs) in various applications is accompanied by a significant increase in computation and parameter storage costs. Recent efforts to r…
Exploring the Back Alleys: Analysing The Robustness of Alternative Neural Network Architectures against Adversarial Attacks
Yi Xiang Marcus Tan, Yuval Elovici, Alexander Binder
We investigate to what extent alternative variants of Artificial Neural Networks (ANNs) are susceptible to adversarial attacks. We analyse the adversarial robustness of conventiona…
Towards Best Practice in Explaining Neural Network Decisions with LRP
Maximilian Kohlbrenner, Alexander Bauer, Shinichi Nakajima +3
Within the last decade, neural network based predictors have demonstrated impressive - and at times super-human - capabilities. This performance is often paid for with an intranspa…
Deep Semi-Supervised Anomaly Detection
Lukas Ruff, Robert A. Vandermeulen, Nico Görnitz +4
Deep approaches to anomaly detection have recently shown promising results over shallow methods on large and complex datasets. Typically anomaly detection is treated as an unsuperv…
Unmasking Clever Hans Predictors and Assessing What Machines Really Learn
Sebastian Lapuschkin, Stephan Wäldchen, Alexander Binder +3
Current learning machines have successfully solved hard application problems, reaching high accuracy and displaying seemingly "intelligent" behavior. Here we apply recent technique…