List of scientific works and research publications developed in the context of the iToBoS project.
- Software for Dataset-wide XAI: From Local Explanations to Global Insights with Zennit, CoRelAy, and ViRelAy.
- Towards the Interpretability of Deep Learning Models for Human Neuroimaging.
- Finding and removing Clever Hans: Using explanation methods to debug and improve deep models.
- Explain and improve: LRP-inference fine-tuning for image captioning models.
- PatClarC: Using pattern concept activation vectors for noise-robust model debugging.
- Quantus: An Explainable AI Toolkit for Responsible Evaluation of Neural Network Explanations.
- Beyond Explaining: Opportunities and Challenges of XAI-Based Model Improvement.
- ECQx: Explainability-Driven Quantization for Low-Bit and Sparse DNNs.
- Explaining Machine Learning Models for Clinical Gait Analysis.
- Explainable AI Methods - A Brief Overview.
- Explaining the Predictions of Unsupervised Learning Models.
- CLEVR-XAI: A benchmark dataset for the ground truth evaluation of neural network explanations.
- Explain to Not Forget: Defending Against Catastrophic Forgetting with XAI.
- Registration of polarimetric images for in vivo skin diagnostics.
- Measurably Stronger Explanation Reliability via Model Canonization.