Transfer learning
Transfer learning involves using knowledge gained from a source task or domain to aid learning. Instead of starting from scratch, transfer learning leverages pre-existing information, such as labeled data or pre-trained models, to bootstrap the learning process and improve the performance of the target task. Transfer learning offers several advantages in the labeling process of machine learning:
- Reduced labeling effort: By leveraging pre-existing labeled data, transfer learning reduces the need for the manual labeling of a large amount of data for the target task. It enables the reuse of knowledge from related tasks, domains, or datasets, saving time and effort in acquiring new labels.
- Improved model performance: Transfer learning allows the target model to benefit from the knowledge learned by a source model. The source model might have been trained on a large, labeled dataset or a different but related task, providing valuable insights and patterns that...
