no code implementations • 24 May 2024 • 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 being able to paint structures with unconditionally trained GANs.
no code implementations • 17 May 2024 • Rudolf Herdt, Louisa Kinzel, Johann Georg Maaß, Marvin Walther, Henning Fröhlich, Tim Schubert, Peter Maass, Christian Patrick Schaaf
Rodents employ a broad spectrum of ultrasonic vocalizations (USVs) for social communication.
no code implementations • 2 Apr 2024 • Rudolf Herdt, Maximilian Schmidt, Daniel Otero Baguer, Peter Maaß
In this work, we investigate methods to reduce the noise in deep saliency maps coming from convolutional downsampling, with the purpose of explaining how a deep learning model detects tumors in scanned histological tissue samples.
no code implementations • 4 Feb 2023 • Rudolf Herdt, Maximilian Schmidt, Daniel Otero Baguer, Jean Le'Clerc Arrastia, Peter Maass
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 a 1x1 convolution.