2 citations · 2 across the 2 of their papers we have counts for
2 papers
cs.LG2024
Reactive Model Correction: Mitigating Harm to Task-Relevant Features via Conditional Bias Suppression
Dilyara Bareeva, Maximilian Dreyer, Frederik Pahde +2
Deep Neural Networks are prone to learning and relying on spurious correlations in the training data, which, for high-risk applications, can have fatal consequences. Various approa…
cs.CV2023★ 2 cited
Reveal to Revise: An Explainable AI Life Cycle for Iterative Bias Correction of Deep Models
Frederik Pahde, Maximilian Dreyer, Wojciech Samek +1
State-of-the-art machine learning models often learn spurious correlations embedded in the training data. This poses risks when deploying these models for high-stake decision-makin…