Comprehensive coverage of knowledge distillation and transfer learning for model compression
exploration of recent developments in vision and language learning for advanced applications
Analysis of relational architectures and multi-task learning for broader machine learning paradigms
Application-focused chapters in image processing, computer vision, edge intelligence, and autonomous systems
Suitable for both fundamental and applied research in computational intelligence
Summarized by Shop
Studies in Computational Intelligence Pedrycz, Witold; Chen, Shyi-Ming
The book provides a timely coverage of the paradigm of knowledge distillation—an efficient way of model compression. Knowledge distillation is positioned in a general setting of transfer learning, which effectively learns a lightweight student model from a large teache