Deep Learning, Volume 48 in the Handbook of Statistics series, highlights new advances in the field, with this new volume presenting interesting chapters on a variety of timely topics, including Generative Adversarial Networks for Biometric Synthesis, Data Science and Pattern Recognition, Facial Data Analysis, Deep Learning in Electronics, Pattern Recognition, Computer Vision and Image Processing, Mechanical Systems, Crop Technology and Weather, Manipulating Faces for Identity Theft via Morphing and Deepfake, Biomedical Engineering, and more.
1. Cover image - p. i 2. Title page - p. ii 3. Table of Contents - p. iii 4. Series Page - p. iii 5. Copyright - p. iii 6. Contributors - p. iii 7. Preface - p. xiii 8. Section I: Foundations - p. xv 9. Chapter 1: Exact deep learning machines - p. 1 10. Chapter 2: Multiscale representation learning for biomedical analysis - p. 9 11. Chapter 3: Adversarial attacks and robust defenses in deep learning - p. 29 12. Section II: Advanced Methods - p. 58 13. Chapter 4: Deep metric learning for computer vision: A brief overview - p. 59 14. Chapter 5: Source distribution weighted multisource domain adaptation without access to source data - p. 81 15. Section III: Transformative Applications - p. 105 16. Chapter 6: Deep learning methods for scientific and industrial research - p. 127 17. Chapter 7: On bias and fairness in deep learning-based facial analysis - p. 178 18. Chapter 8: Manipulating faces for identity theft via morphing and deepfake: Digital privacy - p. 223 19. Index - p. 241
Deep Learning, Volume 48 in the Handbook of Statistics series, highlights new advances in the field, with this new volume presenting interesting chapters on a variety of timely topics, including Generative Adversarial Networks for Biometric Synthesis, Data Science and Pattern Recognition, Facial Data Analysis, Deep Learning in Electronics, Pattern Recognition, Computer Vision and Image Processing, Mechanical Systems, Crop Technology and Weather, Manipulating Faces for Identity Theft via Morphing and Deepfake, Biomedical Engineering, and more.
1. Cover image - p. i 2. Title page - p. ii 3. Table of Contents - p. iii 4. Series Page - p. iii 5. Copyright - p. iii 6. Contributors - p. iii 7. Preface - p. xiii 8. Section I: Foundations - p. xv 9. Chapter 1: Exact deep learning machines - p. 1 10. Chapter 2: Multiscale representation learning for biomedical analysis - p. 9 11. Chapter 3: Adversarial attacks and robust defenses in deep learning - p. 29 12. Section II: Advanced Methods - p. 58 13. Chapter 4: Deep metric learning for computer vision: A brief overview - p. 59 14. Chapter 5: Source distribution weighted multisource domain adaptation without access to source data - p. 81 15. Section III: Transformative Applications - p. 105 16. Chapter 6: Deep learning methods for scientific and industrial research - p. 127 17. Chapter 7: On bias and fairness in deep learning-based facial analysis - p. 178 18. Chapter 8: Manipulating faces for identity theft via morphing and deepfake: Digital privacy - p. 223 19. Index - p. 241
