Discriminative Hallucination for Multi-Modal Few-Shot Learning. - Frederik Pahde; Moin Nabi; Tassilo Klein; Patrick Jähnichen (ICIP, 2018)

Cross-modal Hallucination for Few-shot Fine-grained Recognition. - Frederik Pahde; Patrick Jähnichen; Tassilo Klein; Moin Nabi (CoRR, 2018)

Self-Paced Adversarial Training for Multimodal Few-Shot Learning. - Frederik Pahde; Oleksiy Ostapenko; Patrick Jähnichen; Tassilo Klein; Moin Nabi (WACV, 2019)

Low-Shot Learning From Imaginary 3D Model. - Frederik Pahde; Mihai Marian Puscas; Jannik Wolff; Tassilo Klein; Nicu Sebe; Moin Nabi (WACV, 2019)

Multimodal Prototypical Networks for Few-shot Learning. - Frederik Pahde; Mihai Marian Puscas; Tassilo Klein; Moin Nabi (WACV, 2021)

PatClArC: Using Pattern Concept Activation Vectors for Noise-Robust Model Debugging. - Frederik Pahde; Leander Weber; Christopher J. Anders; Wojciech Samek; Sebastian Lapuschkin (CoRR, 2022)

Optimizing Explanations by Network Canonization and Hyperparameter Search. - Frederik Pahde; Galip Ümit Yolcu; Alexander Binder; Wojciech Samek; Sebastian Lapuschkin (CVPR Workshops, 2023)

Reveal to Revise: An Explainable AI Life Cycle for Iterative Bias Correction of Deep Models. - Frederik Pahde; Maximilian Dreyer; Wojciech Samek; Sebastian Lapuschkin (MICCAI (2), 2023)

From Hope to Safety: Unlearning Biases of Deep Models by Enforcing the Right Reasons in Latent Space. - Maximilian Dreyer; Frederik Pahde; Christopher J. Anders; Wojciech Samek; Sebastian Lapuschkin (CoRR, 2023)

From Hope to Safety: Unlearning Biases of Deep Models via Gradient Penalization in Latent Space. - Maximilian Dreyer; Frederik Pahde; Christopher J. Anders; Wojciech Samek; Sebastian Lapuschkin (AAAI, 2024)

Reactive Model Correction: Mitigating Harm to Task-Relevant Features via Conditional Bias Suppression. - Dilyara Bareeva; Maximilian Dreyer; Frederik Pahde; Wojciech Samek; Sebastian Lapuschkin (CVPR Workshops, 2024)

Synthetic Generation of Dermatoscopic Images with GAN and Closed-Form Factorization. - Rohan Reddy Mekala; Frederik Pahde; Simon Baur; Sneha Chandrashekar; Madeline Diep; Markus Wenzel 002; Eric L. Wisotzky; Galip Ümit Yolcu; Sebastian Lapuschkin; Jackie Ma; Peter Eisert; Mikael Lindvall; et al. (ECCV Workshops (20), 2024)

Explainable Concept Mappings of MRI: Revealing the Mechanisms Underlying Deep Learning-Based Brain Disease Classification. - Christian Tinauer; Anna Damulina; Maximilian Sackl; Martin Soellradl; Reduan Achtibat; Maximilian Dreyer; Frederik Pahde; Sebastian Lapuschkin; Reinhold Schmidt; Stefan Ropele; Wojciech Samek; Christian Langkammer (xAI (2), 2024)

Navigating Neural Space: Revisiting Concept Activation Vectors to Overcome Directional Divergence. - Frederik Pahde; Maximilian Dreyer; Moritz Weckbecker; Leander Weber; Christopher J. Anders; Thomas Wiegand 001; Wojciech Samek; Sebastian Lapuschkin (ICLR, 2025)

Ensuring Medical AI Safety: Explainable AI-Driven Detection and Mitigation of Spurious Model Behavior and Associated Data. - Frederik Pahde; Thomas Wiegand 001; Sebastian Lapuschkin; Wojciech Samek (CoRR, 2025)

Post-Hoc Concept Disentanglement: From Correlated to Isolated Concept Representations. - Eren Erogullari; Sebastian Lapuschkin; Wojciech Samek; Frederik Pahde (CoRR, 2025)