Prof.
Stefan Roth, PhD
Technische Universität Darmstadt
Visual Inference
Hochschulstraße 10
64289 Darmstadt
Short info
My research focuses on machine learning approaches to understanding and analyzing digital images and videos. My lab and I develop new deep learning models and methods for scene understanding, motion estimation, image editing and synthesis, video analysis, image restoration, and more. We aim to make such approaches robust to a broad range of real-world conditions. To that end, we are incorporating inductive biases through combining deep learning with classical models, endowing deep networks with explicit representations of uncertainty, or adapting pre-trained models to changing test-time circumstances. We also aim to reduce the dependency on labeled data by developing semi-supervised and self-supervised learning pipelines.
Open Science
Boosting Unsupervised Semantic Segmentation with Principal Mask Proposals.
arXiv preprint2404.16818.
Articles
Semantic Self-adaptation: Enhancing Generalization with a Single Sample. Transactions on Machine Learning Research (TMLR).
Fast axiomatic attribution for neural networks.
Advances in Neural Information Processing Systems, 34(2), 19513-19524.