Transfer learning encompasses various strategies, including modifying pre-trained networks by replacing their final layers to suit specific tasks. Techniques like image flipping and brightness adjustments are commonly used to enhance training data across different applications. Standardized pipelines built on TensorFlow facilitate these image manipulations, making them accessible for multiple teams.
