Recent publications

June 2023

Dynamic Multi-View Scene Reconstruction Using Neural Implicit Surface

Decai Chen, Oliver Schreer, Peter Eisert, Ingo Feldmann, Haofei Lu

In this paper, we propose a template-free method to reconstruct surface geometry and appearance using neural implicit representations from multi-view videos. We leverage topology-aware deformation and the signed distance field to learn complex...


June 2023

Preserving Memories of Contemporary Witnesses Using Volumetric Video

Volumetric Video is a novel technology that allows the creation of dynamic 3D models of persons, which can then be integrated in any 3D environment. It is authentic and much more realistic and therefore ideal for the transfer of emotions, facial expressions and gestures, which is highly relevant in the context of preservation of contemporary witnesses and survivors of the Holocaust. Fraunhofer HHI is working on two projects in this cultural heritage. A VR documentary about the last Ger! man survivor of the Holocaust Ernst Grube has been produced together with UFA GmbH. A second project is with Dr. Eva Umlauf, the youngest Jewish survivor in the concentration camp in Auschwitz.

Oliver Schreer, Peter Eisert, Ingo Feldmann, Anna Hilsmann, Sylvain Renault, Marcus Zepp, Wieland Morgenstern, Rodrigo Mauricio Diaz Fernandez, Markus Worchel


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

Deep-Unfolded Adaptive Projected Subgradient Method for MIMO Detection

Jochen Fink, Slawomir Stanczak, Renato L. G. Cavalcante, Zoran Utkovski

Deep-Unfolded Adaptive Projected Subgradient Method for MIMO Detection This paper proposes a MIMO detector based on a deep unfolded superiorized adaptive projected subgradient method (APSM). By learning the design parameters of a superiorized...


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

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

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...


June 2023

Reveal to Revise: An Explainable AI Life Cycle for Iterative Bias Correction of Deep Models

Frederick Pahde, Wojciech Samek, Sebastian Lapuschkin, Maximilian Dreyer

State-of-the-art machine learning models often learn spurious correlations embedded in the training data. This poses risks when deploying these models for high-stake decision-making, such as in medical applications like skin cancer detection. To...


June 2023

The Meta-Evaluation Problem in Explainable AI: Identifying Reliable Estimators with MetaQuantus

Anna Hedström, Wojciech Samek, Sebastian Lapuschkin, Marina M.-C. Höhne, Philine Bommer, Kristoffer K. Wickstrøm

Explainable AI (XAI) is a rapidly evolving field that aims to improve transparency and trustworthiness of AI systems to humans. One of the unsolved challenges in XAI is estimating the performance of these explanation methods for neural networks,...


June 2023

Beyond Explaining: Opportunities and Challenges of XAI-Based Model Improvement

Leander Weber, Wojciech Samek, Alexander Binder, Sebastian Lapuschkin

Explainable Artificial Intelligence (XAI) is an emerging research field bringing transparency to highly complex and opaque machine learning (ML) models. This paper offers a comprehensive overview over techniques that apply XAI practically to...


June 2023

Sydnone Methides: Intermediates between Mesoionic Compounds and Mesoionic N-Heterocyclic Olefins

Sebastian Mummel, Eike Hübner, Felix Lederle, Jan C. Namyslo, Martin Nieger, Andreas Schmidt

Sydnone methides represent an almost unknown class of mesoionic compounds which possess exocyclic carbon substituents instead of oxygen (sydnones) or nitrogen (sydnone imines) in the 5-position of a 1,2,3-oxadiazolium ring. Unsubstituted...


June 2023

Bridging the Gap: Gaze Events as Interpretable Concepts to Explain Deep Neural Sequence Models

Daniel Krakowczyk, Sebastian Lapuschkin, David Robert Reich, Paul Prasse, Lena Ann Jäger, Tobias Scheffer

Recent work in XAI for eye tracking data has evaluated the suitability of feature attribution methods to explain the output of deep neural sequence models for the task of oculomotric biometric identification. In this work, we employ established...


May 2023

Demonstration of a 15-Mode Network Node Supported by a Field-Deployed 15-Mode Fiber

Ruben S. Luis, A. Mecozzi, Colja Schubert, F. Achten, Robert Emmerich, Nicolas Braig-Christophersen, Georg Rademacher, Hideaki Furukawa, Giammarco Di Sciullo, Andrea Marotta, Ralf Stolte, Fabio Graziosi, Cristian Antonelli, Pierre Sillard, Giuseppe Ferri, Benjamin J. Puttnam, Roland Ryf, Lauren Dallachiesa, Satoshi Shinada

Researchers from NICT, University of L’Aquila, Finisar, Prysmian and Nokia Bell Lab demonstrate a 2-line side 15-mode spatial division multiplexing network node based on fifteen 2×2 wavelength cross-connects to direct up to six 5 Tb/s, 15-mode,...


May 2023

Semantic modeling of cell damage prediction: A machine learning approach at human-level performance in dermatology

Patrick Wagner, Jackie Ma, Maximilian Springenberg, Marius Kröger, Rose K. C. Moritz, Johannes Schleusener, Martina C. Meinke

In this work we investigate cell damage in whole slice images of the epidermis. A common way for pathologists to annotate a score, characterising the degree of damage for these samples, is the ratio between healthy and unhealthy nuclei. The...



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