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2020 – today
- 2024
[c9]Maximilian Dreyer, Frederik Pahde, Christopher J. Anders, Wojciech Samek, Sebastian Lapuschkin:
From Hope to Safety: Unlearning Biases of Deep Models via Gradient Penalization in Latent Space. AAAI 2024: 21046-21054- [c8]Dilyara Bareeva, Maximilian Dreyer, Frederik Pahde, Wojciech Samek, Sebastian Lapuschkin:
Reactive Model Correction: Mitigating Harm to Task-Relevant Features via Conditional Bias Suppression. CVPR Workshops 2024: 3532-3541 - [c7]Christian Tinauer
, Anna Damulina, Maximilian Sackl, Martin Soellradl, Reduan Achtibat, Maximilian Dreyer, Frederik Pahde, Sebastian Lapuschkin, Reinhold Schmidt, Stefan Ropele, Wojciech Samek, Christian Langkammer:
Explainable Concept Mappings of MRI: Revealing the Mechanisms Underlying Deep Learning-Based Brain Disease Classification. xAI (2) 2024: 202-216 - [i10]Dilyara Bareeva, Maximilian Dreyer, Frederik Pahde, Wojciech Samek, Sebastian Lapuschkin:
Reactive Model Correction: Mitigating Harm to Task-Relevant Features via Conditional Bias Suppression. CoRR abs/2404.09601 (2024) - [i9]Christian Tinauer, Anna Damulina, Maximilian Sackl, Martin Soellradl, Reduan Achtibat, Maximilian Dreyer, Frederik Pahde, Sebastian Lapuschkin, Reinhold Schmidt, Stefan Ropele, Wojciech Samek, Christian Langkammer:
Explainable concept mappings of MRI: Revealing the mechanisms underlying deep learning-based brain disease classification. CoRR abs/2404.10433 (2024) - 2023
- [c6]Frederik Pahde, Galip Ümit Yolcu, Alexander Binder, Wojciech Samek, Sebastian Lapuschkin:
Optimizing Explanations by Network Canonization and Hyperparameter Search. CVPR Workshops 2023: 3819-3828 - [c5]Frederik Pahde, Maximilian Dreyer, Wojciech Samek, Sebastian Lapuschkin:
Reveal to Revise: An Explainable AI Life Cycle for Iterative Bias Correction of Deep Models. MICCAI (2) 2023: 596-606 - [i8]Frederik Pahde, Maximilian Dreyer, Wojciech Samek, Sebastian Lapuschkin:
Reveal to Revise: An Explainable AI Life Cycle for Iterative Bias Correction of Deep Models. CoRR abs/2303.12641 (2023) - [i7]Maximilian Dreyer, Frederik Pahde, Christopher J. Anders, Wojciech Samek, Sebastian Lapuschkin:
From Hope to Safety: Unlearning Biases of Deep Models by Enforcing the Right Reasons in Latent Space. CoRR abs/2308.09437 (2023) - 2022
- [i6]Frederik Pahde, Leander Weber, Christopher J. Anders, Wojciech Samek, Sebastian Lapuschkin:
PatClArC: Using Pattern Concept Activation Vectors for Noise-Robust Model Debugging. CoRR abs/2202.03482 (2022) - [i5]Frederik Pahde, Galip Ümit Yolcu, Alexander Binder, Wojciech Samek, Sebastian Lapuschkin:
Optimizing Explanations by Network Canonization and Hyperparameter Search. CoRR abs/2211.17174 (2022) - 2021
- [c4]Frederik Pahde, Mihai Marian Puscas, Tassilo Klein, Moin Nabi:
Multimodal Prototypical Networks for Few-shot Learning. WACV 2021: 2643-2652 - 2020
- [i4]Frederik Pahde, Mihai Marian Puscas, Tassilo Klein, Moin Nabi:
Multimodal Prototypical Networks for Few-shot Learning. CoRR abs/2011.08899 (2020)
2010 – 2019
- 2019
- [c3]Frederik Pahde, Oleksiy Ostapenko, Patrick Jähnichen, Tassilo Klein, Moin Nabi:
Self-Paced Adversarial Training for Multimodal Few-Shot Learning. WACV 2019: 218-226 - [c2]Frederik Pahde, Mihai Marian Puscas, Jannik Wolff, Tassilo Klein, Nicu Sebe, Moin Nabi:
Low-Shot Learning From Imaginary 3D Model. WACV 2019: 978-985 - [i3]Frederik Pahde, Mihai Marian Puscas, Jannik Wolff, Tassilo Klein, Nicu Sebe, Moin Nabi:
Low-Shot Learning from Imaginary 3D Model. CoRR abs/1901.01868 (2019) - 2018
- [c1]Frederik Pahde, Moin Nabi, Tassilo Klein, Patrick Jähnichen:
Discriminative Hallucination for Multi-Modal Few-Shot Learning. ICIP 2018: 156-160 - [i2]Frederik Pahde, Patrick Jähnichen, Tassilo Klein, Moin Nabi:
Cross-modal Hallucination for Few-shot Fine-grained Recognition. CoRR abs/1806.05147 (2018) - [i1]Frederik Pahde, Oleksiy Ostapenko, Patrick Jähnichen, Tassilo Klein, Moin Nabi:
Self Paced Adversarial Training for Multimodal Few-shot Learning. CoRR abs/1811.09192 (2018)
Coauthor Index
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