Mathis Hoffmann

Lehrstuhl für Informatik 5 (Mustererkennung) Institute Materials for Electronics and Energy Technology (i-MEET)

Addressing data scarcity in nanomaterial segmentation networks with differentiable rendering and generative modeling (2025) Possart D, Mill L, Vollnhals F, Hildebrand T, Suter P, Hoffmann M, Utz J, et al. Journal article PV module power prediction by deep learning on electroluminescence images - Assessing the physics learned by a convolutional neural network (2024) Lüer L, Forberich K, Hepp J, Buerhop-Lutz C, Winkler T, Rodrigues S, Hoffmann M, et al. Journal article Anomaly detection in IR images of PV modules using supervised contrastive learning (2022) Bommes L, Hoffmann M, Buerhop-Lutz C, Pickel T, Hauch J, Brabec C, Maier A, Peters IM Journal article Module-Power Prediction from PL Measurements using Deep Learning (2021) Hoffmann M, Hepp J, Doll B, Buerhop-Lutz C, Peters IM, Brabec C, Maier A, Christlein V Conference contribution, Conference Contribution Outdoor photoluminescence of large area photovoltaic modules (2021) Doll B, Hepp J, Hoffmann M, Talkenberg F, Schüler R, Baier M, Buerhop-Lutz C, et al. Conference contribution, Conference Contribution Joint Super-Resolution and Rectification for Solar Cell Inspection (2021) Hoffmann M, Köhler T, Doll B, Schebesch F, Talkenberg F, Peters IM, Brabec C, et al. Journal article, Original article Deep Learning-based Pipeline for Module Power Prediction from EL Measurements (2021) Hoffmann M, Buerhop-Lutz C, Reeb L, Pickel T, Winkler T, Doll B, Würfl T, et al. Journal article, Original article Photoluminescence for Defect Detection on Full-Sized Photovoltaic Modules (2021) Doll B, Hepp J, Hoffmann M, Schuler R, Buerhop-Lutz C, Peters IM, Hauch J, et al. Journal article Calibration-free beam hardening reduction in x-ray CBCT using the epipolar consistency condition and physical constraints. (2019) Würfl T, Hoffmann M, Aichert A, Maass N, Dennerlein F, Maier A Journal article, Original article Applying Deep Learning Algorithms to EL-images for Predicting the Module Power (2019) Buerhop-Lutz C, Hoffmann M, Reeb L, Pickel T, Hauch J, Maier A Conference contribution, Abstract of lecture