Sayak Paul
Machine Learning Street Talk (MLST)
Exploring Fine-Tuning and Parameter Significance in Transfer Learning
This chapter explores the intricacies of fine-tuning and pruning in transfer learning, emphasizing the balance between generalization and task-specific learning. The discussion includes the implications of pruning based on gradient movement and the necessity for further research to formalize significance for effective transfer learning.
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