@conference {676,
	title = {Incremental one-class learning using regularized null-space training for industrial defect detection},
	booktitle = {16th International Conference on Machine Vision (ICMV)},
	year = {2023},
	month = {11/2023},
	abstract = {One-class incremental learning is a special case of class-incremental learning, where only a single novel class is incrementally added to an existing classifier instead of multiple classes.
This case is relevant in industrial defect detection scenarios, where novel defects usually appear during operation. 
Existing rolled-out classifiers must be updated incrementally in this scenario with only a few novel examples.
In addition, it is often required that the base classifier must not be altered due to approval and warranty restrictions.
While simple finetuning often gives the best performance across old and new classes, it comes with the drawback of potentially losing performance on the base classes (catastrophic forgetting).
Simple prototype approaches work without changing existing weights and perform very well when the classes are well separated but fail dramatically when not.
In theory, null-space training (NSCL) should retain the basis classifier entirely, as parameter updates are restricted to the null space of the network with respect to existing classes.
However, as we show, this technique promotes overfitting in the case of one-class incremental learning.
In our experiments, we found that unconstrained weight growth in null space is the underlying issue, leading us to propose a regularization term (R-NSCL) that penalizes the magnitude of amplification.
The regularization term is added to the standard classification loss and stabilizes null-space training in the one-class scenario by counteracting overfitting. 
We test the method{\textquoteright}s capabilities on two industrial datasets, namely AITEX and MVTec, and compare the performance to state-of-the-art algorithms for class-incremental learning.},
	author = {Matthias Hermann and Georg Umlauf and Bastian Goldl{\"u}cke and Matthias O.  Franz}
}
@conference {668,
	title = {DeepDoubt - Improving uncertainty measures in machine learning to improve explainability and transparency},
	booktitle = {2022 All-Hands-Meeting of the BMBF-funded AI Research Projects at Munich Center for Machine Learning},
	year = {2022},
	author = {Matthias Hermann and Daniel Dold and Georg Umlauf and Oliver D{\"u}rr}
}
@article {670,
	title = {Fast and efficient image novelty detection based on mean-shifts},
	journal = {Sensors | Unusual Behavior Detection Based on Machine Learning },
	year = {2022},
	author = {Matthias Hermann and Georg Umlauf and Bastian Goldl{\"u}cke and Matthias O. Franz}
}
@conference {664,
	title = {Fast and memory-efficient independent component analysis using Lie group techniques},
	booktitle = {International Conference on Curves and Surfaces},
	year = {2022},
	author = {Matthias Hermann and Georg Umlauf and Matthias O. Franz}
}
@conference {663,
	title = {Image novelty detection based on mean-shift and typical set size},
	booktitle = {21th International Conference on Image Analysis and Processing, ICIAP},
	year = {2022},
	author = {Matthias Hermann and Bastian Goldl{\"u}cke and Matthias O. Franz}
}
@conference {649,
	title = {Large-scale independent component analysis by speeding up Lie group techniques},
	booktitle = { International Conference on Acoustics, Speech, and Signal Processing, ICASSP},
	year = {2022},
	author = {Matthias Hermann and Georg Umlauf and Matthias O. Franz}
}
@conference {665,
	title = {Targetless Lidar-camera registration using patch-wise mutual information},
	booktitle = {International Conference on Information Fusion},
	year = {2022},
	author = {Matthias Hermann and Dennis Griesser and Bernhard Gundel and Daniel Dold and Georg Umlauf and Matthias O. Franz}
}
@conference {629,
	title = {Biologically-inspired vs. CNN texture representations in novelty detection},
	booktitle = {Applications of Machine Learning 2021},
	year = {2021},
	pages = {118430I},
	publisher = {International Society for Optics and Photonics},
	organization = {International Society for Optics and Photonics},
	author = {Michael Grunwald and Matthias Hermann and Freiberg, Fabian and Matthias O. Franz}
}
@proceedings {626,
	title = {Damage Detection for Port Infrastructure by Means of Machine-Learning-Algorithms},
	year = {2020},
	month = {04/2020},
	author = {Frederic Hake and Matthias Hermann and Hamza Alkhatib and Christian Hesse and Karsten Holste and Georg Umlauf and Gael Kermarrec and Ingo Neumann}
}
@conference {598,
	title = {Optical Surface Detection: A novelty detection approach based on CNN-encoded features},
	booktitle = {SPIE Optics and Photonics},
	year = {2018},
	month = {10/2018},
	pages = {10752 - 10752 - 13},
	author = {Grunwald, Michael and Matthias Hermann and Freiberg, Fabian and Matthias O. Franz}
}
