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Frederik Pahde
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Multimodal Prototypical Networks for Few-shot Learning
F Pahde, M Puscas, T Klein, M Nabi
WACV 2021, 2020
742020
Cross-modal Hallucination for Few-shot Fine-grained Recognition
F Pahde, P Jähnichen, T Klein, M Nabi
CVPR 2018 Workshop on Fine-grained Visual Categorization, 2018
242018
Discriminative hallucination for multi-modal few-shot learning
F Pahde, M Nabi, T Klein, P Jahnichen
2018 25th IEEE International Conference on Image Processing (ICIP), 156-160, 2018
202018
Self Paced Adversarial Training for Multimodal Few-shot Learning
F Pahde, O Ostapenko, P Jähnichen, T Klein, M Nabi
WACV 2019, 2018
182018
Low-Shot Learning from Imaginary 3D Model
F Pahde, M Puscas, J Wolff, T Klein, N Sebe, M Nabi
WACV 2019, 2019
102019
Reveal to revise: An explainable ai life cycle for iterative bias correction of deep models
F Pahde, M Dreyer, W Samek, S Lapuschkin
Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 …, 2023
82023
Optimizing explanations by network canonization and hyperparameter search
F Pahde, GÜ Yolcu, A Binder, W Samek, S Lapuschkin
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2023
62023
Low-shot learning from imaginary 3D model
F Pahde, M Puscas, M Nabi, T Klein
US Patent 11,080,560, 2021
52021
Navigating Neural Space: Revisiting Concept Activation Vectors to Overcome Directional Divergence
F Pahde, M Dreyer, L Weber, M Weckbecker, CJ Anders, T Wiegand, ...
arXiv preprint arXiv:2202.03482, 2024
4*2024
Self-paced adversarial training for multimodal and 3D model few-shot learning
F Pahde, O Ostapenko, T Klein, M Nabi, M Puscas
US Patent 10,990,848, 2021
22021
From Hope to Safety: Unlearning Biases of Deep Models via Gradient Penalization in Latent Space
M Dreyer, F Pahde, CJ Anders, W Samek, S Lapuschkin
arXiv preprint arXiv:2308.09437, 2023
2023
Optimizing Explanations by Network Canonization and Hyperparameter Search-Supplementary Material
F Pahde, GU Yolcu, A Binder, W Samek, S Lapuschkin
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Artikelen 1–12