Transfer learning: Leveraging trained models on novel tasks
This chapter provides a brief introduction to transfer learning with history and its importance.
As data collection and labeling in a new domain are challenging, transfer learning can play
a vital role to build a reusable model. After explaining the fundamentals of transfer learning,
the strategies are presented followed by different pre-trained models in the fields of
computer vision and natural language processing. We explored prominent models like VGG-
16, Inception, ULMFiT, and BERT. After mentioning the successful models, applications and …
As data collection and labeling in a new domain are challenging, transfer learning can play
a vital role to build a reusable model. After explaining the fundamentals of transfer learning,
the strategies are presented followed by different pre-trained models in the fields of
computer vision and natural language processing. We explored prominent models like VGG-
16, Inception, ULMFiT, and BERT. After mentioning the successful models, applications and …
Abstract
This chapter provides a brief introduction to transfer learning with history and its importance. As data collection and labeling in a new domain are challenging, transfer learning can play a vital role to build a reusable model. After explaining the fundamentals of transfer learning, the strategies are presented followed by different pre-trained models in the fields of computer vision and natural language processing. We explored prominent models like VGG-16, Inception, ULMFiT, and BERT. After mentioning the successful models, applications and limitations of transfer learning have been discussed.
Springer
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