Frederik Pahde

27 papers A* 2A 3B 1Journal 14Unranked 7
YearRankTypeTitle / Venue / Authors
2025 J jnl
Ensuring Medical AI Safety: Explainable AI-Driven Detection and Mitigation of Spurious Model Behavior and Associated Data.
CoRR
Frederik Pahde, Thomas Wiegand, Sebastian Lapuschkin, Wojciech Samek
2025 J jnl
Ensuring medical AI safety: interpretability-driven detection and mitigation of spurious model behavior and associated data.
Mach. Learn.
Frederik Pahde, Thomas Wiegand, Sebastian Lapuschkin, Wojciech Samek
2025 A* conf
Navigating Neural Space: Revisiting Concept Activation Vectors to Overcome Directional Divergence.
ICLR
Frederik Pahde, Maximilian Dreyer, Moritz Weckbecker, Leander Weber, Christopher J. Anders, Thomas Wiegand, Wojciech Samek, Sebastian Lapuschkin
2025 J jnl
Post-Hoc Concept Disentanglement: From Correlated to Isolated Concept Representations.
CoRR
Eren Erogullari, Sebastian Lapuschkin, Wojciech Samek, Frederik Pahde
2025 — conf
Post-hoc Concept Disentanglement: From Correlated to Isolated Concept Representations.
xAI (1)
Eren Erogullari, Sebastian Lapuschkin, Wojciech Samek, Frederik Pahde
2025 — conf
Towards Robust Skin Lesion Classification: Lesion Segmentation, Mole Collision Simulation and Hierarchical Learning.
CMMCA@MICCAI
Hang Nguyen, Paul Fricker, Marianne Defresne, Frederik Pahde, Serena Bonin, Jonathan Wolfe, Eros Azzalini, Iris Zalaudek, Skye Tanzmann, Zung Nguyen
2024 — conf
Explainable Concept Mappings of MRI: Revealing the Mechanisms Underlying Deep Learning-Based Brain Disease Classification.
xAI (2)
Christian Tinauer, Anna Damulina, Maximilian Sackl, Martin Soellradl, Reduan Achtibat, Maximilian Dreyer, Frederik Pahde, Sebastian Lapuschkin, Reinhold Schmidt, Stefan Ropele, Wojciech Samek, Christian Langkammer
2024 J jnl
Explainable concept mappings of MRI: Revealing the mechanisms underlying deep learning-based brain disease classification.
CoRR
Christian Tinauer, Anna Damulina, Maximilian Sackl, Martin Soellradl, Reduan Achtibat, Maximilian Dreyer, Frederik Pahde, Sebastian Lapuschkin, Reinhold Schmidt, Stefan Ropele, Wojciech Samek, Christian Langkammer
2024 A* conf
From Hope to Safety: Unlearning Biases of Deep Models via Gradient Penalization in Latent Space.
AAAI
Maximilian Dreyer, Frederik Pahde, Christopher J. Anders, Wojciech Samek, Sebastian Lapuschkin
2024 — conf
Reactive Model Correction: Mitigating Harm to Task-Relevant Features via Conditional Bias Suppression.
CVPR Workshops
Dilyara Bareeva, Maximilian Dreyer, Frederik Pahde, Wojciech Samek, Sebastian Lapuschkin
2024 J jnl
Reactive Model Correction: Mitigating Harm to Task-Relevant Features via Conditional Bias Suppression.
CoRR
Dilyara Bareeva, Maximilian Dreyer, Frederik Pahde, Wojciech Samek, Sebastian Lapuschkin
2024 — conf
Synthetic Generation of Dermatoscopic Images with GAN and Closed-Form Factorization.
ECCV Workshops (20)
Rohan Reddy Mekala, Frederik Pahde, Simon Baur, Sneha Chandrashekar, Madeline Diep, Markus Wenzel, Eric L. Wisotzky, Galip Ümit Yolcu, Sebastian Lapuschkin, Jackie Ma, Peter Eisert, Mikael Lindvall, Adam A. Porter, Wojciech Samek
2024 J jnl
Synthetic Generation of Dermatoscopic Images with GAN and Closed-Form Factorization.
CoRR
Rohan Reddy Mekala, Frederik Pahde, Simon Baur, Sneha Chandrashekar, Madeline Diep, Markus Wenzel, Eric L. Wisotzky, Galip Ümit Yolcu, Sebastian Lapuschkin, Jackie Ma, Peter Eisert, Mikael Lindvall, Adam A. Porter, Wojciech Samek
2023 J jnl
From Hope to Safety: Unlearning Biases of Deep Models by Enforcing the Right Reasons in Latent Space.
CoRR
Maximilian Dreyer, Frederik Pahde, Christopher J. Anders, Wojciech Samek, Sebastian Lapuschkin
2023 — conf
Optimizing Explanations by Network Canonization and Hyperparameter Search.
CVPR Workshops
Frederik Pahde, Galip Ümit Yolcu, Alexander Binder, Wojciech Samek, Sebastian Lapuschkin
2023 — conf
Reveal to Revise: An Explainable AI Life Cycle for Iterative Bias Correction of Deep Models.
MICCAI (2)
Frederik Pahde, Maximilian Dreyer, Wojciech Samek, Sebastian Lapuschkin
2023 J jnl
Reveal to Revise: An Explainable AI Life Cycle for Iterative Bias Correction of Deep Models.
CoRR
Frederik Pahde, Maximilian Dreyer, Wojciech Samek, Sebastian Lapuschkin
2022 J jnl
Optimizing Explanations by Network Canonization and Hyperparameter Search.
CoRR
Frederik Pahde, Galip Ümit Yolcu, Alexander Binder, Wojciech Samek, Sebastian Lapuschkin
2022 J jnl
PatClArC: Using Pattern Concept Activation Vectors for Noise-Robust Model Debugging.
CoRR
Frederik Pahde, Leander Weber, Christopher J. Anders, Wojciech Samek, Sebastian Lapuschkin
2021 A conf
Multimodal Prototypical Networks for Few-shot Learning.
WACV
Frederik Pahde, Mihai Marian Puscas, Tassilo Klein, Moin Nabi
2020 J jnl
Multimodal Prototypical Networks for Few-shot Learning.
CoRR
Frederik Pahde, Mihai Marian Puscas, Tassilo Klein, Moin Nabi
2019 A conf
Low-Shot Learning From Imaginary 3D Model.
WACV
Frederik Pahde, Mihai Marian Puscas, Jannik Wolff, Tassilo Klein, Nicu Sebe, Moin Nabi
2019 J jnl
Low-Shot Learning from Imaginary 3D Model.
CoRR
Frederik Pahde, Mihai Marian Puscas, Jannik Wolff, Tassilo Klein, Nicu Sebe, Moin Nabi
2019 A conf
Self-Paced Adversarial Training for Multimodal Few-Shot Learning.
WACV
Frederik Pahde, Oleksiy Ostapenko, Patrick Jähnichen, Tassilo Klein, Moin Nabi
2018 J jnl
Cross-modal Hallucination for Few-shot Fine-grained Recognition.
CoRR
Frederik Pahde, Patrick Jähnichen, Tassilo Klein, Moin Nabi
2018 B conf
Discriminative Hallucination for Multi-Modal Few-Shot Learning.
ICIP
Frederik Pahde, Moin Nabi, Tassilo Klein, Patrick Jähnichen
2018 J jnl
Self Paced Adversarial Training for Multimodal Few-shot Learning.
CoRR
Frederik Pahde, Oleksiy Ostapenko, Patrick Jähnichen, Tassilo Klein, Moin Nabi