2.8k citations · 13.3k across the 90 of their papers we have counts for
23 papers · 1 filter
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…
Revealing Hidden Context Bias in Segmentation and Object Detection through Concept-specific Explanations
Maximilian Dreyer, Reduan Achtibat, Thomas Wiegand +2
Applying traditional post-hoc attribution methods to segmentation or object detection predictors offers only limited insights, as the obtained feature attribution maps at input lev…
Optimizing Explanations by Network Canonization and Hyperparameter Search
Frederik Pahde, Galip Ümit Yolcu, Alexander Binder +2
Explainable AI (XAI) is slowly becoming a key component for many AI applications. Rule-based and modified backpropagation XAI approaches however often face challenges when being ap…
Navigating Neural Space: Revisiting Concept Activation Vectors to Overcome Directional Divergence
Frederik Pahde, Maximilian Dreyer, Leander Weber +5
With a growing interest in understanding neural network prediction strategies, Concept Activation Vectors (CAVs) have emerged as a popular tool for modeling human-understandable co…
On the Robustness of Pretraining and Self-Supervision for a Deep Learning-based Analysis of Diabetic Retinopathy
Vignesh Srinivasan, Nils Strodthoff, Jackie Ma +3
There is an increasing number of medical use-cases where classification algorithms based on deep neural networks reach performance levels that are competitive with human medical ex…
Explanation-Guided Training for Cross-Domain Few-Shot Classification
Jiamei Sun, Sebastian Lapuschkin, Wojciech Samek +3
Cross-domain few-shot classification task (CD-FSC) combines few-shot classification with the requirement to generalize across domains represented by datasets. This setup faces chal…