NAPC: a neural algorithm for automated passenger counting in public transport on a privacy-friendly dataset R Seidel, N Jahn, S Seo, T Goerttler, K Obermayer IEEE Open Journal of Intelligent Transportation Systems 3, 33-44, 2021 | 6 | 2021 |
Exploring the Similarity of Representations in Model-Agnostic Meta-Learning T Goerttler, K Obermayer Learning to Learn workshop at ICLR 2021, 2021 | 3 | 2021 |
Similarity of Pre-trained and Fine-tuned Representations T Goerttler, K Obermayer Workshop of Updatable Machine Learning at ICML 2022, 2022 | 2 | 2022 |
Transfer Learning for Segmentation Problems: Choose the Right Encoder and Skip the Decoder J Dippel, M Lenga, T Goerttler, K Obermayer, J Höhne arXiv preprint arXiv:2207.14508, 2022 | 1 | 2022 |
Learning a Multimodal Prior Distribution for Generative Adversarial Nets T Goerttler, M Kloft Proceedings of the Conference on LWDA, 94-105, 2019 | 1 | 2019 |
How much meta-learning is in image-to-image translation? M Eißler, T Goerttler, K Obermayer The Second Blogpost Track at ICLR 2023, 2023 | | 2023 |
Strategies for Classification Layer Initialization in Model-Agnostic Meta-Learning NT Siegel, T Goerttler, K Obermayer The Second Blogpost Track at ICLR 2023, 2023 | | 2023 |
How does the inductive bias influence the generalization capability of neural networks? C Barth, T Goerttler, K Obermayer The Second Blogpost Track at ICLR 2023, 2023 | | 2023 |
Towards Efficient Gradient-Based Meta-Learning in Heterogenous Environments T Goerttler, L Müller, K Obermayer | | 2023 |
Intrinsic Analysis of Learned Representations in Encoder-Decoder Architectures S Durbha, T Goerttler, E Vellasques, JH Stockemer, K Obermayer Proceedings of the Conference on LWDA, 67-78, 2022 | | 2022 |
Berlin-APC: A privacy-friendly dataset for automated passenger counting in public transport R Seidel, D Zarafeta, M Siebert, R Dastgheib Shirazi, S Seo, T Goerttler, ... | | 2021 |
An Interactive Introduction to Model-Agnostic Meta-Learning L Müller, M Ploner, T Goerttler, K Obermayer Workshop on Visualization for AI Explainability at IEEE VIS, https …, 2021 | | 2021 |