Explainable Deep Learning AI

1. Introduction<br>2. Explainable Deep Learning: Methods, Concepts and New Developments <br>3. Compact Visualization of DNN Classification Performances for Interpretation and Improvement <br>4. Explaining How Deep Neural Networks Forget by Deep Visualization <br>5. Characterizing a scene recognition model by identifying the effect of input features via semantic- wise attribution <br>6. A Feature Understanding Method for Explanation of Image Classification by Convolutional Neural Networks <br>7. Explainable Deep Learning for decrypting disease signature in Multiple Sclerosis <br>8. Explanation of CNN Image Classifiers with Hiding Parts <br>9. Remove to Improve? <br>10. Explaining CNN classifier using Association Rule Mining Methods on time-series <br>11. A Methodology to compare XAI Explanations on Natural Language Processing <br>12. Improving Malware Detection with Explainable Machine Learning <br>13. AI Explainability. A Bridge between Machine Vision and Natural Language Processing <br>14. Explainable Deep Learning for Multimedia Indexing and Retrieval <br>15. User Tests and Techniques for the Post-Hoc Explainability of Deep Learning Models <br>16. Conclusion