4 citations · 14 across the 10 of their papers we have counts for
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cs.LG2023
From Hope to Safety: Unlearning Biases of Deep Models via Gradient Penalization in Latent Space
Maximilian Dreyer, Frederik Pahde, Christopher J. Anders +2
Deep Neural Networks are prone to learning spurious correlations embedded in the training data, leading to potentially biased predictions. This poses risks when deploying these mod…
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…