Christoph ReichI'm an ELLIS Ph.D. student at the Technical University of Munich and TU Darmstadt with co-supervision from the University of Oxford. My research focuses on unsupervised scene understanding and self-supervised learning. I'm supervised by Daniel Cremers (CVG), Stefan Roth (VisInf), and Christian Rupprecht (VGG). Prior to starting my Ph.D., I worked as a research intern at NEC Laboratories America (Princeton). Together with my supervisor Biplob Debnath, I worked on controlling standard video codecs for current computer vision models using self-supervised learning. During my studies at TU Darmstadt, I work at the Self-Organizing Systems Lab (Heinz Koeppl) with Tim Prangemeier on 2D and 3D segmentation methods for biomedical applications and generative adversarial networks for live-cell in silico experiments. I also collaborated with the Artificial Intelligent Systems in Medicine Lab (Christoph Hoog Antink) focusing on ECG classification using deep learning. I received a bachelor's in Information Systems Technology (computer science U electrical engineering) and a master's in Autonomous Systems (computer science) from TU Darmstadt. During my bachelor, I work as a working student at Bosch and as a teaching assistant at the mathematics department and the E5 Lab. |
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News
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ResearchI'm interested in computer vision and deep learning. First author publications highlighted. |
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A Perspective on Deep Vision Performance with Standard Image and Video CodecsChristoph Reich, Oliver Hahn, Daniel Cremers, Stefan Roth, Biplob Debnath IEEE/CVF Computer Vision and Pattern Recognition Conference Workshops (CVPRW), 2024 paper arXiv slides poster BibTeX |
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Deep Video Codec Control for Vision ModelsChristoph Reich, Biplob Debnath, Deep Patel, Tim Prangemeier, Daniel Cremers, Srimat Chakradhar IEEE/CVF Computer Vision and Pattern Recognition Conference Workshops (CVPRW), 2024 paper arXiv slides poster BibTeX |
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Deep Image Codec Control for Vision ModelsChristoph Reich Technische Universität Darmstadt, Visual Inference Lab, Master Thesis, 2023 arXiv talk slides BibTeX |
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Transformer Network with Time Prior for Predicting Clinical Outcome from EEG of Cardiac Arrest PatientsMaurice Rohr, Tobias Schilke, Laurent Willems, Christoph Reich, Sebastian Dill, Gökhan Güney, Christoph Hoog Antink 50th Computing in Cardiology Conference (CinC), 2023 paper BibTeX |
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Differentiable JPEG: The Devil is in the DetailsChristoph Reich, Biplob Debnath, Deep Patel, Srimat Chakradhar IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2024 paper arXiv project page code video slides poster BibTeX |
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The TYC Dataset for Understanding Instance-Level Semantics and Motions of Cells in MicrostructuresChristoph Reich, Tim Prangemeier, Heinz Koeppl IEEE/CVF International Conference on Computer Vision Workshops (ICCVW), 2023 paper arXiv project page code slides poster dataset BibTeX |
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An Instance Segmentation Dataset of Yeast Cells in MicrostructuresChristoph Reich*, Tim Prangemeier*, André O. Françani, Heinz Koeppl 45th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), full paper, 2023 (* equal contribution) paper arXiv project page code slides dataset BibTeX |
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On the Atrial Fibrillation Detection Performance of ECG-DualNetChristoph Reich, Maurice Rohr, Tim Kircher, Christoph Hoog Antink 45th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Short Paper, 2023 paper code poster BibTeX |
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Exploring Novel Algorithms for Atrial Fibrillation Detection by Driving Graduate Level Education in Medical Machine LearningMaurice Rohr, Christoph Reich, Andreas Höhl, Timm Lilienthal, Tizian Dege, Filip Plesinger, Veronika Bulkova, Gari D Clifford, Matthew A Reyna, Christoph Hoog Antink Physiological Measurement, 2022 paper code BibTeX |
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Histopathological Image Classification based on Self-Supervised Vision Transformer and Weak LabelsAhmet Gokberk Gul*, Oezdemir Cetin*, Christoph Reich, Nadine Flinner, Tim Prangemeier, Heinz Koeppl Medical Imaging: Digital and Computational Pathology, 2022 (* equal contribution) paper arXiv code BibTeX |
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Yeast Cell Segmentation in Microstructured Environments with Deep LearningTim Prangemeier, Christian Wildner, André O. Françani, Christoph Reich, Heinz Koeppl Biosystems (CIBCB special issue), 2022 paper code BibTeX |
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OSS-Net: Memory Efficient High Resolution Semantic Segmentation of 3D Medical DataChristoph Reich, Tim Prangemeier, Ă–zdemir Cetin, Heinz Koeppl British Machine Vision Conference (BMVC), 2021 paper arXiv project page code video slides BibTeX |
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Multi-StyleGAN: Towards Image-Based Simulation of Time-Lapse Live-Cell MicroscopyChristoph Reich*, Tim Prangemeier*, Christian Wildner, Heinz Koeppl International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), 2021 (* equal contribution) paper supplement arXiv project page code slides poster dataset BibTeX |
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Generation and Simulation of Yeast Microscopy Imagery with Deep LearningChristoph Reich Technische Universität Darmstadt, Self-Organizing Systems Lab, Bachelor Thesis, 2020 arXiv award BibTeX |
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Attention-Based Transformers for Instance Segmentation of Cells in MicrostructuresTim Prangemeier, Christoph Reich, Heinz Koeppl IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2020 paper arXiv code BibTeX |
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Multiclass Yeast Segmentation in Microstructured Environments with Deep LearningTim Prangemeier, Christian Wildner, André O. Françani, Christoph Reich, Heinz Koeppl IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), 2020 paper arXiv BibTeX |
Other projectThese include side projects, open-source contributions, and coursework. Smaller projects can be found at my GitHub profile. |
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Talk - Attention, GPT, ViT, and ChatGPTTechnische Universität Darmstadt, Self-Organizing Systems Lab, 2023 slides video Lab talk on Attention, GPT, ViT, and ChatGPT. |
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MaxViT: Multi-Axis Vision Transformer REIMPLEMENTATIONPrivate project, 2022 code Reimplementation of the paper “MaxViT: Multi-Axis Vision Transformer” by Zhengzhong Tu et al. (image taken from paper). |
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Lightweight Probabilistic Deep Networks for Cell SegmentationTechnische Universität Darmstadt, AI & ML Lab, 2022 slides video Probabilistic graphical models course project supervised by Prof. Kristian Kersting. Utilizing Lightweight Probabilistic Deep Networks by Gast and Roth for trapped yeast cell segmentation. |
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Swin Transformer V2: Scaling Up Capacity and Resolution REIMPLEMENTATIONPrivate project, 2022 code Reimplementation of the paper “Swin Transformer V2: Scaling Up Capacity and Resolution” by Ze Liu et al. (image taken from paper). Code merged into the PyTorch image models library by Ross Wightman (release). |
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ECG-DualNet: Atrial Fibrillation Classification in Electrocardiography using Deep LearningTechnische Universität Darmstadt, KIS*MED, 2021 paper code slides Project seminar supervised by Prof. Christoph Hoog Antink and Maurice Rohr on classifying atrial fibrillation in ECG signals using Deep Learning |
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Dirac-GAN REIMPLEMENTATIONTechnische Universität Darmstadt, MEC-Lab, 2021 code Course project supervised by Anirban Mukhopadhyay reimplementation of the Dirac-GAN proposed by Lars Mescheder et al. in the paper “Which Training Methods for GANs do actually Converge?” published at ICML 2018 |
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Involution: Inverting the Inherence of Convolution for Visual Recognition REIMPLEMENTATIONPrivate project, 2021 code Reimplementation of the CVPR 2021 paper “Inverting the Inherence of Convolution for Visual Recognition” (2D and 3D Involution) by Duo Li et al. (image taken from paper) |
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Mode Collapse Example for Various GAN LossesPrivate project, 2021 code Implementation of various GAN losses to showcase mode collapse in 2D |
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DeepFovea++: Reconstruction and Super-Resolution for Natural Foveated Rendered VideosTechnische Universität Darmstadt, AI & ML Lab, 2020 code slides report Deep learning course project supervised by Prof. Kristian Kersting (group project with Marius Memmel and Jonas Grebe) |
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ToeffiPy a PyTorch like autograd/deep learning library based on NumPyPrivate project, 2020 code Building a PyTorch like autograd/deep learning library based only on NumPy |
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Semantic Pyramid for Image Generation PyTorch REIMPLEMENTATIONPrivate project, 2020 code Reimplementation of the CVPR 2020 paper “Semantic Pyramid for Image Generation” by Assaf Shocher et al. |
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Scalable 3D Semantic Segmentation for Gun Detection in CT ScansTechnische Universität Darmstadt, Visual Inference, 2020 arxiv code report Project lab deep learning in computer vision supervised by Prof. Stefan Roth and Faraz Saeedan (group project with Marius Memmel and Nicolas Wagner) |
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Convolutional Neural Network Application for Cell Segmentation in Biological ResearchTechnische Universität Darmstadt, Self-Organizing Systems Lab, 2019 report Project seminar supervised by Prof. Heinz Koeppl, Tim Prangemeier and Christian Wildner |
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Design / source code from Jon Barron's website © Christoph Reich 2024 |