activity
20232025
most citedModel Stitching and Visualization How GAN Generators can Invert Networks in Real-Time

2 citations · 2 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CV2025

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…

cs.CV2024

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…

cs.SD2024

Enhancing the analysis of murine neonatal ultrasonic vocalizations: Development, evaluation, and application of different mathematical models

Rudolf Herdt, Louisa Kinzel, Johann Georg Maaß +5

Rodents employ a broad spectrum of ultrasonic vocalizations (USVs) for social communication. As these vocalizations offer valuable insights into affective states, social interactio…

cs.CV2024

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…

cs.CV2023★ 2 cited

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…