2 citations · 2 across the 5 of their papers we have counts for
4 papers · 1 filter
Concept Based Explanations and Class Contrasting
Rudolf Herdt, Daniel Otero Baguer
Explaining deep neural networks is challenging, due to their large size and non-linearity. In this paper, we introduce a concept-based explanation method, in order to explain the p…
Visualize and Paint GAN Activations
Rudolf Herdt, Peter Maass
We investigate how generated structures of GANs correlate with their activations in hidden layers, with the purpose of better understanding the inner workings of those models and b…
Smooth Deep Saliency
Rudolf Herdt, Maximilian Schmidt, Daniel Otero Baguer +1
In this work, we investigate methods to reduce the noise in deep saliency maps coming from convolutional downsampling. Those methods make the investigated models more interpretable…
Model Stitching and Visualization How GAN Generators can Invert Networks in Real-Time
Rudolf Herdt, Maximilian Schmidt, Daniel Otero Baguer +2
In this work, we propose a fast and accurate method to reconstruct activations of classification and semantic segmentation networks by stitching them with a GAN generator utilizing…