Recent publications

June 2023

Comparison of Polarization Diversity Configurations of SOI Strip Waveguide-Based Dual-Polarization Wavelength Conversion for S-Band Transmission

Isaac Sackey, Colja Schubert, Carsten Schmidt-Langhorst, Robert Elschner, Tomoyuki Kato, Takeshi Hoshida, Gregor Ronniger, Hidenobu Muranaka, Shun Okada, Yu Tanaka, Tsuyoshi Yamamoto

Using wavelength conversion of our fabricated SOI strip waveguide, we compared experimentally the polarization-insensitive configuration toward S-band real-time transmission. It is found that parallel configuration is 3dB superior in in-out...


June 2023

Explainable Sequence-to-Sequence GRU Neural Network for Pollution Forecasting

Sara Mirzavand Borujeni, Wojciech Samek, Leila Arras, Vignesh Srinivasan

The goal of pollution forecasting models is to allow the prediction and control of the air quality. While such deep learning models were deemed for a long time as black boxes, recent advances in eXplainable AI (XAI) allow to look through the...


June 2023

Multi-View Mesh Reconstruction with Neural Deferred Shading

Markus Worchel, Oliver Schreer, Peter Eisert, Ingo Feldmann, Rodrigo Mauricio Diaz Fernandez, Weiwen Hu

We propose an analysis-by-synthesis method for fast multi-view 3D reconstruction of opaque objects with arbitrary materials and illumination. We represent surfaces as triangle meshes and build a differentiable rendering pipeline around triangle...


June 2023

Increasing the power and spectral efficiencies of an OFDM-based VLC system through multi-objective optimization

Wesley Da Silva Costa, Volker Jungnickel, Ronald Freund, Anagnostis Paraskevopoulos, Malte Hinrichs, Higor Camporez, Maria Pontes, Marcelo Segatto, Helder Rocha, Jair Silva

In order to minimize power usage and maximize spectral efficiency in visible light communication (VLC), we use a multi-objective optimization algorithm and compare DC-biased optical OFDM (DCO-OFDM) with constant envelope OFDM (CE-OFDM)...


June 2023

Optimizing Explanations by Network Canonization and Hyperparameter Search

Frederick Pahde, Wojciech Samek, Alexander Binder, Sebastian Lapuschkin, Galip Ümit Yolcu

Rule-based and modified backpropagation XAI methods struggle with innovative layer building blocks and implementation-invariance issues. 

In this work we propose canonizations for popular deep neural network architectures and...


June 2023

Experimental Demonstration of Optical Modulation Format Identification Using SOI-based Photonic Reservoir

Guillermo von Hünefeld, Colja Schubert, Ronald Freund, Johannes Fischer, Isaac Sackey, Gregor Ronniger, Pooyan Safari, Md Mahasin Khan, Rijil Thomas, Enes Seker, Stephan Suckow, Max Lemme, David Stahl

We experimentally show modulation format identification in the optical domain using Silicon-on-Insulator-based Photonic-Integrated-Circuit (PIC) reservoir. Identification of 32 GBd single-polarization signals of 4QAM, 16QAM, 32QAM and 64QAM is...


June 2023

Fooling State-of-the-Art Deepfake Detection with High-Quality Deepfakes

Arian Beckmann, Peter Eisert, Anna Hilsmann

Due to the rising threat of deepfakes to security and privacy, it is most important to develop robust and reliable detectors. In this paper, we examine the need for high-quality samples in the training datasets of such detectors. Accordingly, we...


June 2023

Assessing the Value of Multimodal Interfaces: A Study on Human–Machine Interaction in Weld Inspection Workstations

Paul Chojecki, Peter Eisert, Sebastian Bosse, Detlef Runde, David Przewozny, Niklas Gard, Niklas Hoerner, Dominykas Strazdas, Ayoub Al-Hamadi

Multimodal user interfaces promise natural and intuitive human–machine interactions. However, is the extra effort for the development of a complex multisensor system justified, or can users also be satisfied with only one input modality? This...


June 2023

Shortcomings of Top-Down Randomization-Based Sanity Checks for Evaluations of Deep Neural Network Explanations

Alexander Binder, Klaus-Robert Müller, Wojciech Samek, Grégoire Montavon, Sebastian Lapuschkin, Leander Weber

While the evaluation of explanations is an important step towards trustworthy models, it needs to be done carefully, and the employed metrics need to be well-understood. Specifically model randomization testing is often overestimated and regarded...


June 2023

Revealing Hidden Context Bias in Segmentation and Object Detection through Concept-specific Explanations

Maximilian Dreyer, Thomas Wiegand, Wojciech Samek, Sebastian Lapuschkin, Reduan Achtibat

Applying traditional post-hoc attribution methods to segmentation or object detection predictors offers only limited insights, as the obtained feature attribution maps at input level typically resemble the models' predicted segmentation mask or...



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