Explainable Deep Learning AI

Jenny Benois-Pineau is a professor of computer science at the University of Bordeaux and head of the “Video Analysis and Indexing” research group of the “Image and Sound” team of LABRI UMR 58000 Université Bordeaux / CNRS / IPB-ENSEIRB. She was deputy scientific director of theme B of the French national research unit CNRS GDR ISIS (2008-2015) and is currently in charge of international relations at the College of Sciences and Technologies of the University of Bordeaux. She obtained her doctorate in Signals and Systems in Moscow and her Habilitation to Direct Research in Computer Science and Image Processing at the University of Nantes in France. Her subjects of interest include image and video analysis and indexing, artificial intelligence methods applied to image recognition. Since 2009 he’s been an Associate Professor in the Computer Science Department of the IUT ("Technical School"), University of Bordeaux (Talence), France. He is also deputy director of the BKB ("Bench to Knowledge and Beyond") team of LaBRI. Dragutin Petkovic is Professor in the Computer Science department at San Francisco State University, USA. Senior researcher at CNRS, leader of the MRIM group. Works at the Laboratory of Informatics of Grenoble and Multimedia Information Indexing and Retrieval Group.

• 1. Introduction2. Explainable Deep Learning: Methods, Concepts and New Developments 3. Compact Visualization of DNN Classification Performances for Interpretation and Improvement 4. Explaining How Deep Neural Networks Forget by Deep Visualization 5. Characterizing a scene recognition model by identifying the effect of input features via semantic- wise attribution 6. A Feature Understanding Method for Explanation of Image Classification by Convolutional Neural Networks 7. Explainable Deep Learning for decrypting disease signature in Multiple Sclerosis 8. Explanation of CNN Image Classifiers with Hiding Parts 9. Remove to Improve? 10. Explaining CNN classifier using Association Rule Mining Methods on time-series 11. A Methodology to compare XAI Explanations on Natural Language Processing 12. Improving Malware Detection with Explainable Machine Learning 13. AI Explainability. A Bridge between Machine Vision and Natural Language Processing 14. Explainable Deep Learning for Multimedia Indexing and Retrieval 15. User Tests and Techniques for the Post-Hoc Explainability of Deep Learning Models 16. Conclusion