Transfer Learning: Leveraging the Capability of Pre-trained Models Across Different DomainsBoD – Books on Demand, 12 Mar 2025 - 126 halaman This edited volume explores the potential of transfer learning in advancing artificial intelligence (AI) across diverse domains. Transfer learning enables AI systems to leverage knowledge gained from one task to enhance performance in another, significantly reducing data requirements and training time while improving model efficiency. The book presents the latest approaches for implementing transfer learning in various contexts, from telecommunications and brain-computer interfaces to quantum computing applications. Readers will discover innovative techniques for domain adaptation, cross-domain knowledge transfer, and hybrid classical-quantum implementations. The text addresses critical challenges in making transfer learning more explainable, reliable, and scalable, particularly concerning privacy preservation and computational efficiency. Key topics include AI-native networks, neural network transfer learning, domain adaptation strategies, and quantum machine learning integration. Both theoretical frameworks and practical implementations are discussed, making this book valuable for researchers, practitioners, and students interested in developing more efficient and capable AI systems. The content bridges the gap between theoretical understanding and practical application, offering insights into how transfer learning can be effectively deployed in real-world scenarios. By examining transfer learning through multiple lenses, from traditional neural networks to quantum computing, this volume provides a unique perspective on the future of AI development and its potential to revolutionize various technological sectors. |
Isi
29 | 12 |
Transfer Learning for NonInvasive BCI EEG Brainwave Decoding | 29 |
Chapter 4 | 49 |
Chapter 5 | 63 |
Chapter 6 | 95 |
Istilah dan frasa umum
3GPP accuracy action advising AI-native network algorithms analysis applications approach Artificial Intelligence arXiv arXiv preprint base station brain brain-computer interfaces brainwave brainwave decoding cancer cardiovascular challenges chapter computer vision convolutional neural networks deep learning deep transfer learning demonstrated diabetes dietary patterns dietary structure disease diverse domain adaptation EEG data EEG decoding EEG signals efficiency Engineering enhance environment federated learning Figure fine-tuning food preferences food structure homomorphic encryption IEEE IEEE Transactions image classification improve input intake IntechOpen International Conference knowledge labeled layers learning models leveraging model trained motor imagery multiple obesity parameters Pennylane plant-based potential pre-trained models quantum circuit quantum computing quantum machine learning quantum transfer learning random forest reinforcement learning risk selection significant smartphone source model specific support vector machine target domain tasks techniques telecommunication networks tion transfer learning transformer utilizing Wang Yelp Zhang