Deep Learning

Erscheinungsjahr 2023
ISBN: 978-0-443-18430-7
Verlag: Elsevier Science & Technology

Buch, Englisch, 420 Seiten, Format (B × H): 152 mm x 229 mm, Gewicht: 1000 g

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.

Zielgruppe

Statisticians, mathematicians and students

Autoren/Hrsg.

Weitere Mitwirkende

Govindaraju, Venu

Fachgebiete

• Mathematik | Informatik Mathematik Stochastik

Inhaltsverzeichnis

1. Exact deep learning machines
Arni S.R. Srinivasa Rao
2. Multiscale representation learning for biomedical analysis
Abhishek Singh, Utkarsh Porwal, Anurag Bhardwaj, and Wei Jin
3. Adversarial attacks and robust defenses in deep learning
Chun Pong Lau, Jiang Liu, Wei-An Lin, Hossein Souri, Pirazh Khorramshahi, and Rama Chellappa
4. Deep metric learning for computer vision: A brief overview
Deen Dayal Mohan, Bhavin Jawade, Srirangaraj Setlur, and Venu Govindaraju
5. Source distribution weighted multisource domain adaptation without access to source data
Sk Miraj Ahmed, Dripta S. Raychaudhuri, Samet Oymak, and Amit K. Roy-Chowdhury
6. Deep learning methods for scientific and industrial research
G.K. Patra, Kantha Rao Bhimala, Ashapurna Marndi, Saikat Chowdhury, Jarjish Rahaman, Sutanu Nandi, Ram Rup Sarkar, K.C. Gouda, K.V. Ramesh, Rajesh P. Barnwal, Siddhartha Raj, and Anil Saini
7. On bias and fairness in deep learning-based facial analysis
Surbhi Mittal, Puspita Majumdar, Mayank Vatsa, and Richa Singh
8. Manipulating faces for identity theft via morphing and deepfake: Digital privacy
Akshay Agarwal and Nalini Ratha

Über Autor(innen)

Govindaraju, Venu
Dr. Venu Govindaraju, SUNY Distinguished Professor of Computer Science and Engineering, is the Vice President of Research and Economic Development of the University at Buffalo and founding director of the Center for Unified Biometrics and Sensors. He received his Bachelor's degree with honors from the Indian Institute of Technology (IIT) in 1986, and his Ph.D. from UB in 1992. His research focus is on machine learning and pattern recognition in the domains of Document Image Analysis and Biometrics. Dr. Govindaraju has co-authored about 400 refereed scientific papers. His seminal work in handwriting recognition was at the core of the first handwritten address interpretation system used by the US Postal Service. He was also the prime technical lead responsible for technology transfer to the Postal Services in US, Australia, and UK. He has been a Principal or Co-Investigator of sponsored projects funded for about 65 million dollars. Dr. Govindaraju has supervised the dissertations of 30 doctoral students. He has served on the editorial boards of premier journals such as the IEEE Transactions on Pattern Analysis and Machine Intelligence and is currently the Editor-in-Chief of the IEEE Biometrics Council Compendium. Dr. Govindaraju is a Fellow of the ACM (Association of Computing Machinery), IEEE (Institute of Electrical and Electronics Engineers), AAAS (American Association for the Advancement of Science), the IAPR (International Association of Pattern Recognition), and the SPIE (International Society of Optics and Photonics). He is recipient of the 2004 MIT Global Indus Technovator award and the 2010 IEEE Technical Achievement award.