54 citations · 128 across the 8 of their papers we have counts for
9 papers
A Rigorous Study Of The Deep Taylor Decomposition
Leon Sixt, Tim Landgraf
Saliency methods attempt to explain deep neural networks by highlighting the most salient features of a sample. Some widely used methods are based on a theoretical framework called…
DNNR: Differential Nearest Neighbors Regression
Youssef Nader, Leon Sixt, Tim Landgraf
K-nearest neighbors (KNN) is one of the earliest and most established algorithms in machine learning. For regression tasks, KNN averages the targets within a neighborhood which pos…
Do Users Benefit From Interpretable Vision? A User Study, Baseline, And Dataset
Leon Sixt, Martin Schuessler, Oana-Iuliana Popescu +2
A variety of methods exist to explain image classification models. However, whether they provide any benefit to users over simply comparing various inputs and the model's respectiv…
Analyzing a Caching Model
Leon Sixt, Evan Zheran Liu, Marie Pellat +4
Machine Learning has been successfully applied in systems applications such as memory prefetching and caching, where learned models have been shown to outperform heuristics. Howeve…
Two4Two: Evaluating Interpretable Machine Learning - A Synthetic Dataset For Controlled Experiments
Martin Schuessler, Philipp Weiß, Leon Sixt
A growing number of approaches exist to generate explanations for image classification. However, few of these approaches are subjected to human-subject evaluations, partly because…
Restricting the Flow: Information Bottlenecks for Attribution
Karl Schulz, Leon Sixt, Federico Tombari +1
Attribution methods provide insights into the decision-making of machine learning models like artificial neural networks. For a given input sample, they assign a relevance score to…
When Explanations Lie: Why Many Modified BP Attributions Fail
Leon Sixt, Maximilian Granz, Tim Landgraf
Attribution methods aim to explain a neural network's prediction by highlighting the most relevant image areas. A popular approach is to backpropagate (BP) a custom relevance score…
Automatic localization and decoding of honeybee markers using deep convolutional neural networks
Benjamin Wild, Leon Sixt, Tim Landgraf
The honeybee is a fascinating model animal to investigate how collective behavior emerges from (inter-)actions of thousands of individuals. Bees may acquire unique memories through…
RenderGAN: Generating Realistic Labeled Data
Leon Sixt, Benjamin Wild, Tim Landgraf
Deep Convolutional Neuronal Networks (DCNNs) are showing remarkable performance on many computer vision tasks. Due to their large parameter space, they require many labeled samples…